Pareto Approximation Empirical Results of Energy-Aware Optimization for Precedence-Constrained Task Scheduling Considering Switching Off Completely Idle Machines

Abstract

Recent advances in cloud computing, large language models, and deep learning have started a race to create massive High-Performance Computing (HPC) centers worldwide. These centers increase in energy consumption proportionally to their computing capabilities; for example, according to the top 500 organization, the HPC centers Frontier, Aurora, and Super Computer Fugaku report energy consumptions of 22,786 kW, 38,698 kW, and 29,899 kW, respectively. Currently, energy-aware scheduling is a topic of interest to many researchers. However, as far as we know, this work is the first approach considering the idle energy consumption by the HPC units and the possibility of turning off unused units entirely, driven by a quantitative objective function. We found that even when turning off unused machines, the objectives of makespan and energy consumption still conflict and, therefore, their multi-objective optimization nature. This work presents empirical results for AGEMOEA, AGEMOEA2, GWASFGA, MOCell, MOMBI, MOMBI2, NSGA2, and SMS-EMOA. The best-performing algorithm is MOCell for the 400 real scheduling problem tests. In contrast, the best-performing algorithm is GWASFGA for a small-instance synthetic testbed.

1. Introduction

Energy consumption is becoming a more and more relevant problem in High-Performance Computing (HPC) systems. According to [1], humanity produces 328.77 million terabytes of data daily, with an exponentially growing trend. Our transition to cloud storage has developed in just a few years, gaining relevance since the launch of Amazon Web Services in 2006. Similar services, like Google Drive and Apple’s iCloud, focusing on regular users rather than developers, quickly followed, offering storage services for a monthly fee. The above services are, in reality, large farms of servers or, in other words, HPC systems that compute sequential and parallel applications. Furthermore, with the massive utilization of artificial intelligence and large language models, companies like Alibaba, Bytedance, and Tesla, among many others, are increasing their computing capabilities through cloud servers [2] and HPC systems. However, as stated in [1], HPC systems have a tremendous environmental impact. For example, in Arizona, Google made a deal that allowed them to use 1 million gallons of water daily to cool their HPC. In addition, Google’s Arizona HPC needs a considerable amount of energy to operate. Google’s HPC in Arizona alone consumes more than 430 megawatts of energy; a few years ago, their energy consumption was significantly lower [3]. Therefore, more research is necessary to maximize energy consumption efficiency and computing times for HPC systems, especially now that even data centers have millions of computing units. Several approaches in the literature use the Dynamic Voltage Frequency Scaling [4] (DVFS) technique to reduce the computing units’ voltage when it is unnecessary. However, as far as we know, this is the first study to consider switching off completely idle machines exclusively guided by their total energy consumption, i.e., direct energy consumption, energy consumption at idle times, and turning off machines that are not necessary for reducing computing times or energy savings. The scheduling HPC problem formulation for the parallel application computing time is the one in [5]. At the same time, the energy model is an improvement on the one in [4]. We formulate the problem as a multi-objective optimization problem because reducing the computing time and producing energy savings are conflicting objectives. The formulation is validated using eight well-known multi-objective optimization algorithms from the state-of-the-art. As far as we know, this is the first approach to turning off unused machines of an HPC system entirely guided by the whole system’s energy consumption, using a quantitative objective function without uncertainties or assumptions about the energy consumption and considering the consumption of every machine in the system even at idle times. Moreover, we study our proposed energy formulation for the first time as a multi-objective approach with the conflicting objective of the parallel application computation time. We empirically prove that the objectives conflict even when turning off unused hardware. Therefore, the problem is a multi-objective optimization problem with non-dominated solutions producing Pareto front approximations. Our study’s limitations lie within the domain of multi-objective optimization research. Our goal is to identify a set of non-dominated solutions that is accurate, diverse, representative, and close to the real Pareto Front.

This work’s main contributions are as follows: (i) a new energy objective function for HPC systems considering energy idle consumption and the possibility of turning off unused machines; (ii) an empirical study of the Pareto front approximations achieved by state-of-the-art algorithms for multi-objective optimization.

The remainder of the paper is organized as follows: Section 1 is the introduction. Section 2 presents a brief review of the different approaches in the literature for minimizing parallel applications’ computing time and their energy consumption, stating the difference between the ones in the state-of-the-art and our proposed approach. Section 3 provides comprehensive details of our studied parallel applications model for computing times and energy consumption and considering switching off idles machines. The description of the eight multi-objective optimization studied algorithms is given in Section 4. Section 5 presents all the necessary details to reproduce our experimentation. Section 6 outlines the experimental results and discusses them. Finally, Section 7 gives insights into the computed results, our conclusions, and future research direction on this work’s proposed approach.

2. Literature Review

Energy-aware scheduling optimization for High-Performance Computing (HPC) heterogeneous systems is highly relevant from both a research perspective and as a real-world scenario. Two kinds of applications involve computing in HPC systems: sequential applications (independent jobs/tasks without precedences) and parallelizable software (a set of jobs/tasks with precedences). A parallel application is usually faster than a sequential version. This work focuses on scheduling parallel software applications, which implies scheduling parallel applications in an HPC heterogeneous system. A parallel application involves precedence constraints between tasks (pieces of information needed before starting consequent tasks) and communications (the size of information to transfer in a communication link between machines). The research community studies different variants of the scheduling problem as follows: (i) Cloud computing [6,7]; according to [8]—the definition of cloud computing is ambiguous, but we consider two main characteristics of cloud computing, one being the unlimited computing resources and two being the consideration of a broker for transactions and costs. (ii) Virtual Machines [9,10]—virtual machines can be part of both cloud computing and HPC systems, but their weakness is the reduction in performance contrary to executing the parallel tasks in bare-metal; virtualization adds a layer of translation between the application and the hardware, reducing the maximum actual performance of the systems. (iii) Energy-aware [11,12,13], among others, mentioned in [5]; in many studies, energy-aware formulations have the flaws of not considering the energy consumption of the machines at idle times, not considering turning off machines, or not considering the possible combinations of turning machines off and on guided by an accurate quantitative formulation, as we do in this work.

This work’s primary focus is multi-objective optimization. The first objective is to minimize the parallel applications’ makespan, while the second is to minimize energy consumption, considering energy consumption in idle time and the possibility of completely turning off unused machines only for cases with improvements in global energy consumption.

Few works in the literature deal with similar approaches to the one we propose in this work. For example, in [14], Cho et al. minimize the number of machines/processors used to schedule parallel applications, similar to our proposed approach. However, in our approach, machines not considered for a scheduling solution (completely idle) are turned off, contrary to the approach of Cho et al., where machines stay idle. Our approach differs because we do not reduce the number of machines used if it does not reduce the High-Performance Computing (HPC) system’s energy consumption or if it increases the parallel application computation time at any magnitude—which are, in most cases, conflicting goals. Moreover, minimizing the number of machines/servers does not always reduce energy consumption, as stated in [15]. Other works with the same drawbacks in reducing the number of machines appear in [16,17,18,19]. To avoid the drawbacks mentioned above, we use a multi-objective approach (a set of optimal solutions instead of a single solution) and an energy consumption formulation, considering all the machine’s energy consumption even when idle and the possibility of turning them off.

Another close approach to our proposal is load balancing [20]. Generally speaking, when performing load balancing, the goal is to evenly spread the work over the available resources, reducing the number of idle machines. In contrast, an unbalanced load concentrates the work in fewer nodes, idling others. The load balancing approach has benefits like fault tolerance or data mirroring availability. The above has the drawback of increasing the energy consumption because of the use of the complete system and even producing a lot of energy consumption at idle time slots in the machines/nodes. In our proposal, the above does not happen because our proposed energy formulation guarantees a reduction in energy consumption by turning off unnecessary machines to achieve the best execution time for parallel applications, with the drawback of no data mirroring and expecting no faults in the system. We focus entirely on the system’s energy consumption and performance, even if the work is unbalanced. Some works following the approach of load balancing are [21,22,23].

Although minimizing energy consumption and minimizing the computing time of parallel applications are conflicting objectives (hence, their multi-objective nature), our approach of directly measuring the energy consumption of the system’s machines, even at idle, has the advantage of certainty about which machines to turn off and if it has benefits in using fewer machines for the computing time and energy consumption. The above is because idle voltage consumption varies from machine to machine in an HPC heterogeneous system. Furthermore, computing units are becoming more reliable and fault-tolerant, reducing the necessity of mirroring data in different nodes/machines and allowing them to turn off unnecessary computing units, providing respective energy savings. The approach followed by this work is to minimize the parallel applications’ computing times without mirroring or duplicating the data and to minimize the total system energy consumption, considering the energy consumption of the machines at idle times and turning off unnecessary machines. Our approach gains relevance as HPC systems increase in computing units, with some having millions of machines demanding energy while staying idle.

Several approaches, such as mathematical programming, heuristics, evolutionary computation, or metaheuristics, are used for precedence-task scheduling in heterogeneous systems. Nevertheless, due to its multiple optimal solutions, evolutionary computation working over a population of solutions is the best approach when studying a multi-objective optimization problem. Therefore, in this work, we study evolutionary algorithms for multi-objective optimization [24,25,26]. Outside the scope are algorithms that depend on a continuous search space, such as differential evolution [27,28] or particle swarm optimization [29]. Section 4 has a selection of high-performance evolutionary algorithms relevant in the state-of-the-art, adaptable to discrete optimization (as is this scheduling problem), and available source code from the jMetal [30,31] project.

3. Parallel Applications Model

This section describes the mathematical formulation of parallel applications on heterogeneous computing systems and how their computing time and energy consumption are calculated.

3.1. Parallel Applications Computing Time

In this work, we follow the parallel application representation used in [4,5,32,33,34,35,36,37]. We can represent a parallel computer application as a Directed Acyclic Graph (DAG) $G=(T,C)$, where T is the set of n tasks $t_i\in T$ of the parallel application, the edges $(t_i,t_j)\in C$ represent their precedences, and their weights represent communication costs between tasks. The communication cost between tasks in the same machine is zero (no communication link usage). A matrix P defines the execution times of the $t_i$ tasks in the machines $m_j\in M$ from a heterogeneous system with different computing capabilities. Given that the system is heterogeneous and machines are Dynamic Voltage Frequency Scalable (DVFS), the computing times $P_{i,j}$ need to be recomputed according to the machine-selected frequency (their relative speed). Therefore, the actual computing time of the ith-task on the jth-machine is $P_{i,j}^{\prime }={\displaystyle \frac{P_{i,j}}{r{s}_k}}$, where their relative speed $r{s}_k$ equals 1.0 for a machine operating at their maximum voltage/speed, meaning 100% of their speed.

Table 1. Speed pair configurations in the experimental comparison.

Speed Pairs 1		Speed Pairs 2		Speed Pairs 3
${\mathit{v}}_{\mathit{k}}$	${\mathit{rs}}_{\mathit{k}}$	${\mathit{v}}_{\mathit{k}}$	${\mathit{rs}}_{\mathit{k}}$	${\mathit{v}}_{\mathit{k}}$	${\mathit{rs}}_{\mathit{k}}$
1.75	1.0	1.50	1.0	2.20	1.0
1.40	0.80	1.40	0.90	1.90	0.85
1.20	0.60	1.30	0.80	1.60	0.65
0.90	0.40	1.20	0.70	1.30	0.50
-	-	1.10	0.60	1.00	0.35
-	-	1.00	0.50	-	-
-	-	0.90	0.40	-	-

illustrates this work’s three voltage/speed configurations. We use the three configurations as a circular list. For example, if we have more than four machines, machine four will have speed pair number 1, machine five will have speed pair number 2, machine six will have speed pair number 3, machine seven will have speed pair number 1, and so on.

Another crucial element to compute the parallel application completion time known as makespan is the order to execute the tasks, which is indeed a topological order of the DAG; further, as stated in [5], not all execution orders would produce the optimal makespan. This work uses the bottom-level (b-level) algorithm, detailed in [4]. The b-level computes the longest path in the DAG from every $t_i\in T$ to the final task; for multiple final tasks, an artificial final dummy task is added, with the multiple final tasks as their precedences, considering the mean computing times in the machines. Ranking the tasks’ b-levels in descending order gives their priority for execution. Using the b-level guarantees that our experiments will not produce unfeasible solutions, that they are reproducible for other researchers, and that they have a typical execution order in the literature.

Using the notation in [5] where w is the beginning time of the first available time window to execute $t_i$ in their assigned $m_k$ machine, the variables $r{t}_i$, $s{t}_i$, and $f{t}_i$ represent the ready-to-execute time, starting time, and finish time of the $t_i$ task, respectively. Using the above notation, Equations (1)–(4) compute the makespan, and taking an execution order into account is essential to determine the available time windows w.

$$\begin{array}{c}\hfill r{t}_i=max\{f{t}_j+C_{j,i}\forall (t_j,t_i)\in C\}\end{array}$$

(1)

$$\begin{array}{c}\hfill s{t}_i=max\left(r{t}_i,\underset{w\ge r{t}_i}{min}\{f{t}_j\le w⊻w\le s{t}_j-P_{i,k}^{\prime }\forall t_j\in m_k\}\right)\end{array}$$

(2)

$$\begin{array}{c}\hfill f{t}_i=s{t}_i+P_{i,k}^{\prime }\end{array}$$

(3)

$$\begin{array}{c}\hfill Makespan=\underset{\forall t_i\in T}{max}\left\{f{t}_i\right\}\end{array}$$

(4)

3.2. Parallel Applications’ Energy Consumption

For the energy consumption model, we slightly modify the one in [4], where it is necessary to know the makespan of the solution to evaluate their energy consumption. The energy consumption equals the square power of the task’s voltage (machines are DVFS capable) multiplied by the task duration ($P_{i,j}^{\prime }$). The modification lies in how it computes the energy consumption in idle time; when a machine does not execute any task, the machine time in idle is equal to the makespan. Therefore, if machine $m_j$ has no scheduled tasks, no energy consumption is considered because we can turn off the machine without affecting the parallel application completion time. Algorithm 1 computes our parallel applications’ energy function.

Algorithm 1 Energy switching off machines objective function.

1: $energy\leftarrow 0$   2: for each  $t_i\in T$  do   3:     $energy+=v_k^2·P_{i,j}^{\prime }$   4: end for   5: for each  $m_j\in M$  do   6:     $idle=makespan$   7:     for each $t_i$ assigned to $m_j$ do   8:         $idle-=P_{i,j}^{\prime }$   9:     end for 10:    if $idle\ne makespan$ then 11:         $energy+=idle·v_{min}^2$ 12:    end if 13: end for

To summarize, Equation (5) computes the direct energy consumption ($E_c$) of the tasks as the sum of their durations’ times $P_{i,j}^{\prime }$ multiplied by their respective voltage $v_k^2$. While Equation (6) computes the idle energy consumption ($E_i$) of the machines $m_j$ while not executing tasks, if the machines $m_j$ do not execute any tasks, their contribution to the sum is equal to zero.

$$Ec=\sum _{i=1}^n{P}_{i,j}^{\prime }·v_k^2$$

(5)

$$Ei=\sum _{j=1}^m\left\{\begin{array}{c}(makespan-P_{i,j}^{\prime })\forall t_i\in m_j\hfill \\ 0\mathrm{if}\mathrm{no}\mathrm{tasks}\mathrm{schedule}\mathrm{in}{m}_j\hfill \end{array}\right.$$

(6)

Finally, Equation (7) computes the total energy consumption ($Et$).

$$Et=Ec+Ei$$

(7)

4. Algorithms in Comparison

This section briefly describes the eight multi-objective optimization algorithms used for the experimental comparison. The algorithms were selected based on performance in the literature and the feasibility of optimizing discrete problems with discrete uniform crossover and boundary mutation. A second criterion for the selection was the availability of their source code in the Jmetal project [30,31].

4.1. AGEMOEA

The Adaptive Geometry Estimation-based MOEA (AGEMOEA [38]) main loop is similar to the one in NSGA-II, replacing the density estimator of crowding distance for a survival score. AGEMOEA estimates the geometry of the Pareto front by finding p in its associated $L_p$ norm (distance). Once p is defined, the solution’s proximity is the $L_p$ norm to the ideal point, and their diversity is the minimum distance between solutions. The solutions’ survival score is a ratio between diversity and proximity.

4.2. AGEMOEA2

The Adaptive Geometry Estimation-based MOEA 2 (AGEMOEA2 [39]) is an improvement over AGEMOEA. AGEMOEA2 incorporates the Newton–Raphson method to determine the value of p in the $L_p$ norm (when $p=2$, the norm is the Euclidean distance). The diversity between solutions is measured using geodesic distance.

4.3. GWASFGA

The Global Weighting Achievement Scalarizing Function Genetic Algorithm (GWASFGA [40]) is an enhancement over WASFGA [41]. GWASFGA uses a set of weights whose inverse are uniform, using them for the environmental selection, as in the multi-objective decomposition [42] approach. GWASFGA evaluates the weights using a scalarizing function formulation of the augmented weighted Tchebycheff, intercalating the reference point in their evaluation between a utopian point (better than the ideal) and a nadir point is slightly worsened. In other words, half of the weights have as reference the utopian point, and the other half the nadir point, slightly worsened.

4.4. MOCell

Multi-objective Cellular (MOCell [43]) adapts the structured population evolutionary algorithm called the cellular genetic algorithm for multi-objective optimization. To adjust the mono-objective algorithm to multi-objective optimization, MOCell includes an external archive with the non-dominated solutions found bounded using the density estimator of crowding distance. The solutions in the archive replace a fixed number of randomly chosen solutions in the structured population at every generation.

4.5. MOMBI

The Many-Objective Metaheuristic Based on the $R2$ Indicator (MOMBI [44]) is a metaheuristic focused on many-objective optimization. MOMBI has an environmental selection based on the $R2$ indicator using the scalarizing function-weighted Tchebycheff. As in multi-objective decomposition algorithms, a set of uniform weights is evaluated, and the best-ranked solutions survive according to the R2 indicator.

4.6. MOMBI2

The Many-Objective Metaheuristic Based on the $R2$ Indicator 2 (MOMBI2 [45]) is an improvement over MOMBI’s loss of diversity for high-dimensionality problems. The enhancements in MOMBI2 are as follows: (i) Using an achievement scalarizing function instead of weighted Tchebycheff. (ii) The Nadir reference point is updated adaptively. (iii) When two individuals contribute equally to the R2 indicator, the one with the lower Euclidean distance survives.

4.7. NSGA2

The Non-dominated Sorting Genetic Algorithm 2 (NSGA2 [46]) adapts genetic algorithms for multi-objective optimization with two key components. (i) The non-dominated sort mechanism, which ranks the population (solutions) into sets of non-dominated solutions (fronts). (ii) The solution density estimator of crowding distance, measuring the fitness room between solutions. Their environmental selection is elitist, and the solutions in the first fronts with greater crowding distance survive.

4.8. SMS-EMOA

S Metric Selection EMOA (SMS-EMOA [47]) is a steady-state multi-objective optimization evolutionary algorithm that uses hypervolume contributions as a solution density estimator. The solution in the last non-dominated front with the minor contribution to the front hypervolume does not survive the environmental selection.

5. Experimental Setup

This section describes the experimental configuration to reproduce our experimental evaluation in detail.

5.1. Numerical Results

Each algorithm solved every scheduling instance problem for 30 independent runs. This paper presents the results as the mean and standard deviation, measures of central tendency and dispersion, respectively, with the format $MEA{N}_{SD}$ in scientific notation, and E is used as a shortcut for $X10$ to represent times ten raised to the power of x. In the results tables, the best and second-best values for every problem are emphasized with dark and light gray backgrounds to identify the best-performing algorithms quickly. In the results table, the first column specifies the instance name, e.g., Robot-8-88-0.1-0.1 is a scheduling problem of the Robot application, with eight machines, 88 tasks, a heterogeneity factor of 0.1, and a cost-to-communication ratio of 0.1.

5.2. Algorithms’ Settings

For the evaluated algorithms in Section 4, we execute the implementations provided by the jMetal framework [30,31], with the same crossover probability ($p_c=1.0$) and mutation probability ($p_m=1/n$). The rest of the parameters of the algorithms are as suggested in their original papers. MOMBI and MOMBI2 use a set of 101 weights produced by the simplex-lattice design. GWASFGA uses the weight vectors generator implemented in jMetal for a population size of 100. The rest of the algorithms use a population size of 100. The stop criterion for all the algorithms is 25,000 function evaluations.

Table 2. Parameter settings for the eight state-of-the-art compared algorithms.

	AGEMOEA	AGEMOEA2
Population size:	100	100
Selection:	Binary Tournament	Binary Tournament
Recombination:	Uniform: $p_r=1.0$	Uniform: $p_r=1.0$
Mutation:	Boundary: $p_m=1/n$	Boundary: $p_m=1/n$
	MOMBI	MOMBI2
Population size:	101	101
Selection:	Binary Tournament	Binary Tournament
Recombination:	Uniform: $p_r=1.0$	Uniform: $p_r=1.0$
Mutation:	Boundary: $p_m=1/n$	Boundary: $p_m=1/n$
	SMS-EMOA	MOCell
Archive size:	-	100
Population size:	100	100
Selection:	Random	Binary Tournament
Recombination:	Uniform: $p_r=1.0$	Uniform: $p_r=1.0$
Mutation:	Boundary: $p_m=1/n$	Boundary: $p_m=1/n$
	GWASFGA	NSGA2
Population size:	100	100
Selection:	Binary Tournament	Binary Tournament
Recombination:	Uniform: $p_r=1.0$	Uniform: $p_r=1.0$
Mutation:	Boundary: $p_m=1/n$	Boundary: $p_m=1/n$

summarizes the parameters of the eight algorithms.

5.3. Evolutionary Operators

For a fair comparison, the eight considered algorithms in Section 4 are implemented using the same evolutionary operators and the same operator settings. The crossover or recombination operator implemented is a uniform discrete crossover [48,49], which stochastically mixes with the same probability of each one of the decision variables of the parents, and the uniform crossover complexity is $O\left(n\right)$. The mutation or perturbation operator is a discrete boundary [34] operator, which replaces the original variable value with a random value between their feasible upper and lower bounds, and the boundary mutation complexity is $O\left(n\right)$. The number of decision variables in the solutions equals the number of tasks, and a decision variable is a pair of integers indicating the assigned machine and voltage selection.

5.4. Multi-Objective Quality Indicators

In multi-objective optimization, there is no single optimal solution; instead, there is a set of solutions known as Pareto optimal. The objective values of the Pareto optimal solutions produce the Pareto front. Different properties are desirable in the Pareto front approximations found by the algorithms. There is no single multi-objective quality indicator that encompasses all the desired characteristics. This work uses three quality indicators: (i) hypervolume [50], to measure the dominated objective space by the Pareto front approximation; (ii) inverted generational distance plus [51], as a measure of the distance of the Pareto front approximation to the Pareto front; (iii) additive epsilon [52], as a measure of the smallest distance to translate the Pareto front approximation to weakly dominate the Pareto front. Since the true Pareto front is unknown, we use the set of non-dominated solutions found from all the executions from all the studied algorithms as the true Pareto front. The above quality indicators give us insights into the performance of the studied algorithms.

5.5. Statistical Test Perform

Due to the stochastic, non-parametric, multiple-sample nature of the computed data, we perform the Friedman statistical test for two reasons: (i) to assess the statistical significance that at least two of the samples represent populations with significantly different means [53]; (ii) to analyze the overall performance of the algorithms using their average rankings [4,54].

5.6. Scheduling Studied Problems

The real-world applications are the same as in [5] with four different applications: Fpppp (double precision floating point FORTRAN benchmark), LIGO (Laser Interferometer Gravitational-Wave Observatory), Robot (Robot control application), and Sparse (Sparse matrix solver). There are 100 instances per real-world application, varying their number of machines (using 8, 16, 32, 64), heterogeneity factor between the machines (using 0.1, 0.25, 0.5, 0.75, 1), and cost-to-communication ratio (CCR) (using 0.1, 0.5, 1, 5, 10) in the Directed Acyclic Graphs. The above give a total of 400 real-world parallel application examples. The real-world application problems use the nomenclature Application–Machines–Tasks–heterogeneity–CCR. The small synthetic benchmark considered in this work is the same as in [5], conformed by 14 synthetic scheduling problems in the literature. The small benchmark nomenclature is Author–Machines–Tasks. Both sets of instances are available at https://github.com/AASantiago/SchedulingInstances accessed on 22 November 2024.

6. Results

This section summarizes the experimental results for the eight studied algorithms on 400 real application scheduling problems and 14 synthetic small instances.

6.1. Numerical Results

First, let us discuss the results for the 400 instances of the four real parallel applications according to each of the three multi-objective quality indicators mentioned in Section 5.4.

Table 3. Hypervolume mean and standard deviation of the eight algorithms over 30 independent runs. Dark/light gray emphasize the best/second-best results.

Problem	AGEMOEA	AGEMOEA2	GWASFGA	MOCell	MOMBI	MOMBI2	NSGA2	SMS-EMOA
Fpppp-8-334-0.1-0.1	$4.7E-{02}_{7.3E-2}$	$4.2E-{02}_{5.9E-2}$	$4.6E-{03}_{1.9E-2}$	$2.6E-{01}_{2.0E-1}$	$6.1E-{03}_{3.0E-2}$	$1.3E-{03}_{4.3E-3}$	$5.4E-{02}_{6.0E-2}$	$4.3E-{03}_{1.6E-2}$
Fpppp-8-334-0.1-0.5	$7.8E-{02}_{8.7E-2}$	$8.4E-{02}_{8.3E-2}$	$6.3E-{03}_{2.4E-2}$	$1.8E-{01}_{1.5E-1}$	$6.4E-{03}_{1.5E-2}$	$3.6E-{03}_{1.0E-2}$	$6.8E-{02}_{8.6E-2}$	$5.7E-{03}_{1.3E-2}$
Fpppp-8-334-0.1-1	$2.1E-{02}_{3.6E-2}$	$9.7E-{03}_{4.4E-2}$	$0.0E{00}_{0.0E0}$	$1.4E-{01}_{1.8E-1}$	$0.0E{00}_{0.0E0}$	$9.2E-{04}_{4.9E-3}$	$2.1E-{02}_{4.0E-2}$	$2.5E-{04}_{1.3E-3}$
Fpppp-8-334-0.1-5	$0.0E{00}_{0.0E0}$	$8.7E-{04}_{4.7E-3}$	$0.0E{00}_{0.0E0}$	$2.1E-{02}_{1.1E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-8-334-0.1-10	$0.0E{00}_{0.0E0}$	$3.1E-{03}_{1.7E-2}$	$0.0E{00}_{0.0E0}$	$1.2E-{02}_{6.6E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-8-334-0.25-0.1	$1.7E-{02}_{4.4E-2}$	$1.4E-{02}_{4.3E-2}$	$9.0E-{05}_{4.9E-4}$	$8.8E-{02}_{1.5E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$4.5E-{03}_{1.7E-2}$	$0.0E{00}_{0.0E0}$
Fpppp-8-334-0.25-0.5	$2.5E-{01}_{1.4E-1}$	$2.2E-{01}_{1.0E-1}$	$5.6E-{02}_{5.9E-2}$	$3.7E-{01}_{1.4E-1}$	$8.3E-{02}_{6.0E-2}$	$9.7E-{02}_{7.2E-2}$	$2.1E-{01}_{6.4E-2}$	$1.1E-{01}_{6.0E-2}$
Fpppp-8-334-0.25-1	$0.0E{00}_{0.0E0}$	$9.9E-{04}_{5.4E-3}$	$0.0E{00}_{0.0E0}$	$2.4E-{02}_{8.9E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$4.8E-{03}_{1.8E-2}$	$0.0E{00}_{0.0E0}$
Fpppp-8-334-0.25-5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-8-334-0.25-10	$0.0E{00}_{0.0E0}$	$1.8E-{03}_{9.5E-3}$	$0.0E{00}_{0.0E0}$	$1.2E-{02}_{5.5E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$4.1E-{03}_{2.2E-2}$	$0.0E{00}_{0.0E0}$
Fpppp-8-334-0.5-0.1	$1.6E-{01}_{9.5E-2}$	$1.6E-{01}_{1.1E-1}$	$4.1E-{02}_{6.9E-2}$	$2.4E-{01}_{1.4E-1}$	$5.0E-{02}_{6.0E-2}$	$5.0E-{02}_{5.9E-2}$	$1.7E-{01}_{1.1E-1}$	$4.3E-{02}_{4.6E-2}$
Fpppp-8-334-0.5-0.5	$6.3E-{02}_{1.2E-1}$	$2.9E-{02}_{5.2E-2}$	$0.0E{00}_{0.0E0}$	$1.8E-{01}_{2.0E-1}$	$9.1E-{04}_{3.8E-3}$	$1.9E-{03}_{6.2E-3}$	$6.0E-{02}_{1.1E-1}$	$1.7E-{03}_{5.3E-3}$
Fpppp-8-334-0.5-1	$9.8E-{03}_{3.6E-2}$	$5.1E-{03}_{2.7E-2}$	$0.0E{00}_{0.0E0}$	$3.7E-{02}_{8.2E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$7.2E-{03}_{2.6E-2}$	$1.1E-{03}_{5.7E-3}$
Fpppp-8-334-0.5-5	$0.0E{00}_{0.0E0}$	$3.5E-{03}_{1.9E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$3.3E-{03}_{1.8E-2}$	$0.0E{00}_{0.0E0}$
Fpppp-8-334-0.5-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.4E-{02}_{7.4E-2}$	$0.0E{00}_{0.0E0}$
Fpppp-8-334-0.75-0.1	$1.6E-{02}_{4.3E-2}$	$1.7E-{02}_{4.0E-2}$	$0.0E{00}_{0.0E0}$	$4.7E-{02}_{9.0E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.2E-{02}_{3.4E-2}$	$6.5E-{04}_{2.7E-3}$
Fpppp-8-334-0.75-0.5	$1.1E-{01}_{1.1E-1}$	$6.9E-{02}_{6.5E-2}$	$2.4E-{02}_{6.3E-2}$	$1.3E-{01}_{1.4E-1}$	$3.5E-{02}_{5.3E-2}$	$3.4E-{02}_{7.0E-2}$	$1.1E-{01}_{1.2E-1}$	$1.2E-{02}_{2.7E-2}$
Fpppp-8-334-0.75-1	$5.7E-{03}_{1.7E-2}$	$1.8E-{02}_{5.1E-2}$	$0.0E{00}_{0.0E0}$	$9.5E-{03}_{3.4E-2}$	$0.0E{00}_{0.0E0}$	$2.2E-{03}_{1.1E-2}$	$1.5E-{02}_{5.7E-2}$	$5.2E-{04}_{2.8E-3}$
Fpppp-8-334-0.75-5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-8-334-0.75-10	$3.0E-{02}_{1.4E-1}$	$1.4E-{03}_{7.3E-3}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.5E-{04}_{7.8E-4}$	$0.0E{00}_{0.0E0}$
Fpppp-8-334-1-0.1	$0.0E{00}_{0.0E0}$	$3.2E-{03}_{1.7E-2}$	$0.0E{00}_{0.0E0}$	$2.0E-{02}_{1.1E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$4.7E-{03}_{2.0E-2}$	$0.0E{00}_{0.0E0}$
Fpppp-8-334-1-0.5	$1.2E-{02}_{3.2E-2}$	$5.6E-{02}_{1.0E-1}$	$0.0E{00}_{0.0E0}$	$3.2E-{02}_{9.3E-2}$	$5.4E-{04}_{2.9E-3}$	$1.1E-{03}_{6.1E-3}$	$4.2E-{02}_{1.0E-1}$	$1.2E-{03}_{4.3E-3}$
Fpppp-8-334-1-1	$4.3E-{03}_{1.7E-2}$	$2.1E-{02}_{4.7E-2}$	$0.0E{00}_{0.0E0}$	$4.7E-{02}_{1.2E-1}$	$0.0E{00}_{0.0E0}$	$2.1E-{03}_{1.2E-2}$	$1.6E-{02}_{6.4E-2}$	$0.0E{00}_{0.0E0}$
Fpppp-8-334-1-5	$1.4E-{02}_{7.7E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-8-334-1-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-16-334-0.1-0.1	$2.8E-{02}_{4.5E-2}$	$6.7E-{02}_{1.2E-1}$	$0.0E{00}_{0.0E0}$	$1.6E-{01}_{1.5E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$3.4E-{02}_{5.5E-2}$	$2.4E-{03}_{1.0E-2}$
Fpppp-16-334-0.1-0.5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-16-334-0.1-1	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$5.5E-{03}_{3.0E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$9.5E-{04}_{5.1E-3}$	$0.0E{00}_{0.0E0}$
Fpppp-16-334-0.1-5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$7.8E-{03}_{4.2E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.6E-{02}_{8.8E-2}$	$0.0E{00}_{0.0E0}$
Fpppp-16-334-0.1-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-16-334-0.25-0.1	$2.9E-{02}_{8.4E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$5.1E-{02}_{1.0E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$7.0E-{03}_{3.5E-2}$	$0.0E{00}_{0.0E0}$
Fpppp-16-334-0.25-0.5	$0.0E{00}_{0.0E0}$	$2.5E-{03}_{1.3E-2}$	$0.0E{00}_{0.0E0}$	$3.3E-{02}_{6.9E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-16-334-0.25-1	$0.0E{00}_{0.0E0}$	$9.5E-{04}_{5.1E-3}$	$0.0E{00}_{0.0E0}$	$1.4E-{02}_{5.9E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-16-334-0.25-5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-16-334-0.25-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-16-334-0.5-0.1	$9.2E-{04}_{4.9E-3}$	$4.2E-{04}_{2.3E-3}$	$0.0E{00}_{0.0E0}$	$2.7E-{02}_{5.3E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.0E-{02}_{2.9E-2}$	$0.0E{00}_{0.0E0}$
Fpppp-16-334-0.5-0.5	$1.2E-{02}_{5.4E-2}$	$9.5E-{03}_{3.6E-2}$	$0.0E{00}_{0.0E0}$	$3.4E-{02}_{6.2E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$6.1E-{03}_{2.7E-2}$	$0.0E{00}_{0.0E0}$
Fpppp-16-334-0.5-1	$1.3E-{03}_{7.0E-3}$	$1.4E-{03}_{6.5E-3}$	$0.0E{00}_{0.0E0}$	$1.9E-{02}_{5.3E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$7.0E-{03}_{3.7E-2}$	$0.0E{00}_{0.0E0}$
Fpppp-16-334-0.5-5	$2.9E-{03}_{1.6E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$2.2E-{03}_{1.2E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$8.4E-{04}_{4.5E-3}$	$0.0E{00}_{0.0E0}$
Fpppp-16-334-0.5-10	$0.0E{00}_{0.0E0}$	$1.3E-{02}_{7.2E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-16-334-0.75-0.1	$1.1E-{02}_{5.8E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.9E-{04}_{1.0E-3}$	$0.0E{00}_{0.0E0}$
Fpppp-16-334-0.75-0.5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$2.4E-{02}_{6.2E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.2E-{04}_{6.4E-4}$	$0.0E{00}_{0.0E0}$
Fpppp-16-334-0.75-1	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.7E-{03}_{9.0E-3}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-16-334-0.75-5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-16-334-0.75-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-16-334-1-0.1	$2.4E-{04}_{1.3E-3}$	$8.0E-{04}_{4.3E-3}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$7.5E-{03}_{2.8E-2}$	$0.0E{00}_{0.0E0}$
Fpppp-16-334-1-0.5	$1.3E-{03}_{6.8E-3}$	$1.9E-{02}_{1.0E-1}$	$0.0E{00}_{0.0E0}$	$4.4E-{02}_{1.5E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.1E-{02}_{4.4E-2}$	$1.6E-{02}_{8.8E-2}$
Fpppp-16-334-1-1	$0.0E{00}_{0.0E0}$	$3.6E-{03}_{1.9E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.6E-{03}_{8.9E-3}$	$0.0E{00}_{0.0E0}$
Fpppp-16-334-1-5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-16-334-1-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$3.0E-{03}_{1.6E-2}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-0.1-0.1	$1.8E-{02}_{8.8E-2}$	$3.4E-{03}_{1.8E-2}$	$0.0E{00}_{0.0E0}$	$4.1E-{02}_{8.6E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$6.9E-{03}_{3.1E-2}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-0.1-0.5	$0.0E{00}_{0.0E0}$	$3.6E-{03}_{1.9E-2}$	$0.0E{00}_{0.0E0}$	$3.2E-{02}_{9.2E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-0.1-1	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-0.1-5	$9.3E-{03}_{5.0E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$3.5E-{02}_{1.1E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-0.1-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-0.25-0.1	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-0.25-0.5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-0.25-1	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-0.25-5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-0.25-10	$2.4E-{02}_{1.3E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-0.5-0.1	$2.6E-{03}_{1.3E-2}$	$1.4E-{02}_{6.4E-2}$	$0.0E{00}_{0.0E0}$	$5.5E-{02}_{1.1E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$8.9E-{03}_{3.4E-2}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-0.5-0.5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-0.5-1	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-0.5-5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-0.5-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-0.75-0.1	$1.8E-{02}_{9.5E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$2.6E-{03}_{1.4E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.0E-{02}_{5.4E-2}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-0.75-0.5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-0.75-1	$1.3E-{02}_{6.8E-2}$	$2.9E-{02}_{1.1E-1}$	$0.0E{00}_{0.0E0}$	$3.4E-{02}_{8.4E-2}$	$2.4E-{04}_{1.3E-3}$	$0.0E{00}_{0.0E0}$	$5.4E-{02}_{1.5E-1}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-0.75-5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-0.75-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-1-0.1	$2.0E-{02}_{1.0E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.0E-{02}_{3.8E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$4.9E-{03}_{2.6E-2}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-1-0.5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-1-1	$4.1E-{03}_{2.2E-2}$	$8.5E-{03}_{3.6E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$7.3E-{03}_{4.0E-2}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-1-5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-1-10	$1.6E-{02}_{8.7E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-64-334-0.1-0.1	$0.0E{00}_{0.0E0}$	$5.9E-{04}_{3.2E-3}$	$0.0E{00}_{0.0E0}$	$3.6E-{02}_{1.2E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.5E-{04}_{8.0E-4}$	$0.0E{00}_{0.0E0}$
Fpppp-64-334-0.1-0.5	$1.9E-{04}_{1.0E-3}$	$3.9E-{04}_{2.1E-3}$	$0.0E{00}_{0.0E0}$	$3.1E-{02}_{7.0E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-64-334-0.1-1	$4.3E-{02}_{9.0E-2}$	$1.2E-{02}_{2.8E-2}$	$0.0E{00}_{0.0E0}$	$1.2E-{01}_{1.6E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$2.4E-{02}_{1.1E-1}$	$0.0E{00}_{0.0E0}$
Fpppp-64-334-0.1-5	$2.6E-{02}_{9.8E-2}$	$1.2E-{02}_{3.7E-2}$	$0.0E{00}_{0.0E0}$	$5.9E-{02}_{1.6E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.4E-{03}_{4.4E-3}$	$1.3E-{03}_{6.8E-3}$
Fpppp-64-334-0.1-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.4E-{02}_{7.5E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-64-334-0.25-0.1	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$4.9E-{02}_{1.3E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$2.2E-{03}_{1.2E-2}$	$0.0E{00}_{0.0E0}$
Fpppp-64-334-0.25-0.5	$7.3E-{04}_{4.0E-3}$	$5.4E-{03}_{2.3E-2}$	$0.0E{00}_{0.0E0}$	$6.3E-{02}_{9.6E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-64-334-0.25-1	$2.0E-{02}_{5.0E-2}$	$3.5E-{03}_{1.8E-2}$	$0.0E{00}_{0.0E0}$	$1.0E-{01}_{1.6E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$8.4E-{03}_{2.9E-2}$	$0.0E{00}_{0.0E0}$
Fpppp-64-334-0.25-5	$1.1E-{02}_{5.8E-2}$	$2.2E-{02}_{1.0E-1}$	$0.0E{00}_{0.0E0}$	$6.9E-{02}_{1.3E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.1E-{02}_{4.8E-2}$	$9.7E-{04}_{5.2E-3}$
Fpppp-64-334-0.25-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.6E-{03}_{8.8E-3}$	$0.0E{00}_{0.0E0}$
Fpppp-64-334-0.5-0.1	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-64-334-0.5-0.5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.1E-{02}_{4.0E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-64-334-0.5-1	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.1E-{02}_{4.5E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-64-334-0.5-5	$3.9E-{04}_{2.1E-3}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$6.9E-{02}_{1.7E-1}$	$3.8E-{03}_{2.0E-2}$	$0.0E{00}_{0.0E0}$	$4.0E-{03}_{2.2E-2}$	$0.0E{00}_{0.0E0}$
Fpppp-64-334-0.5-10	$1.1E-{02}_{5.1E-2}$	$4.7E-{03}_{2.5E-2}$	$0.0E{00}_{0.0E0}$	$9.2E-{03}_{5.0E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-64-334-0.75-0.1	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-64-334-0.75-0.5	$7.4E-{03}_{4.0E-2}$	$2.9E-{03}_{1.6E-2}$	$0.0E{00}_{0.0E0}$	$3.1E-{02}_{9.3E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$5.0E-{03}_{1.8E-2}$	$0.0E{00}_{0.0E0}$
Fpppp-64-334-0.75-1	$2.4E-{03}_{1.3E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$2.1E-{02}_{4.7E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$8.1E-{03}_{4.4E-2}$	$0.0E{00}_{0.0E0}$
Fpppp-64-334-0.75-5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$9.2E-{03}_{3.6E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-64-334-0.75-10	$0.0E{00}_{0.0E0}$	$6.6E-{03}_{3.6E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$7.0E-{03}_{3.8E-2}$	$0.0E{00}_{0.0E0}$
Fpppp-64-334-1-0.1	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-64-334-1-0.5	$1.3E-{02}_{7.2E-2}$	$1.5E-{02}_{8.1E-2}$	$0.0E{00}_{0.0E0}$	$8.6E-{03}_{4.5E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$7.7E-{04}_{4.1E-3}$	$0.0E{00}_{0.0E0}$
Fpppp-64-334-1-1	$0.0E{00}_{0.0E0}$	$6.8E-{04}_{3.6E-3}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-64-334-1-5	$4.1E-{04}_{2.2E-3}$	$2.0E-{02}_{7.8E-2}$	$0.0E{00}_{0.0E0}$	$2.2E-{03}_{9.5E-3}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$2.9E-{04}_{1.5E-3}$	$7.2E-{03}_{3.9E-2}$
Fpppp-64-334-1-10	$1.5E-{02}_{6.7E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.1E-{02}_{5.9E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$

,

Table 4. Hypervolume mean and standard deviation of the eight algorithms over 30 independent runs. Dark/light gray emphasize the best/second-best results.

Problem	AGEMOEA	AGEMOEA2	GWASFGA	MOCell	MOMBI	MOMBI2	NSGA2	SMS-EMOA
LIGO-8-76-0.1-0.1	$4.5E-{02}_{9.2E-2}$	$1.5E-{02}_{5.8E-2}$	$0.0E{00}_{0.0E0}$	$1.9E-{01}_{1.3E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$3.4E-{02}_{9.0E-2}$	$1.1E-{02}_{3.3E-2}$
LIGO-8-76-0.1-0.5	$0.0E{00}_{0.0E0}$	$1.5E-{03}_{7.7E-3}$	$0.0E{00}_{0.0E0}$	$2.3E-{02}_{6.5E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
LIGO-8-76-0.1-1	$1.1E-{02}_{2.6E-2}$	$2.2E-{02}_{8.3E-2}$	$0.0E{00}_{0.0E0}$	$6.6E-{02}_{1.0E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$9.1E-{03}_{3.8E-2}$	$0.0E{00}_{0.0E0}$
LIGO-8-76-0.1-5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
LIGO-8-76-0.1-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$3.5E-{04}_{1.9E-3}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$7.4E-{03}_{3.9E-2}$	$3.9E-{05}_{2.1E-4}$
LIGO-8-76-0.25-0.1	$1.3E-{04}_{6.9E-4}$	$7.0E-{04}_{3.7E-3}$	$0.0E{00}_{0.0E0}$	$2.6E-{02}_{7.0E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
LIGO-8-76-0.25-0.5	$1.5E-{02}_{5.0E-2}$	$6.1E-{02}_{1.0E-1}$	$0.0E{00}_{0.0E0}$	$1.4E-{01}_{1.6E-1}$	$9.5E-{04}_{5.1E-3}$	$6.1E-{04}_{2.4E-3}$	$1.9E-{02}_{7.0E-2}$	$1.0E-{02}_{3.4E-2}$
LIGO-8-76-0.25-1	$2.6E-{01}_{2.0E-1}$	$2.8E-{01}_{1.8E-1}$	$6.3E-{02}_{5.7E-2}$	$3.5E-{01}_{1.8E-1}$	$1.0E-{01}_{9.3E-2}$	$1.1E-{01}_{8.6E-2}$	$3.1E-{01}_{1.6E-1}$	$1.6E-{01}_{1.0E-1}$
LIGO-8-76-0.25-5	$8.8E-{03}_{3.4E-2}$	$6.2E-{03}_{2.4E-2}$	$0.0E{00}_{0.0E0}$	$4.6E-{02}_{1.1E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.9E-{02}_{5.8E-2}$	$4.7E-{03}_{2.5E-2}$
LIGO-8-76-0.25-10	$4.2E-{03}_{2.3E-2}$	$8.0E-{03}_{4.3E-2}$	$0.0E{00}_{0.0E0}$	$1.0E-{02}_{5.6E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
LIGO-8-76-0.5-0.1	$7.8E-{02}_{1.0E-1}$	$8.9E-{02}_{1.0E-1}$	$0.0E{00}_{0.0E0}$	$2.0E-{01}_{1.9E-1}$	$2.0E-{03}_{8.1E-3}$	$6.9E-{03}_{2.0E-2}$	$1.2E-{01}_{1.3E-1}$	$1.5E-{02}_{3.3E-2}$
LIGO-8-76-0.5-0.5	$6.0E-{03}_{2.0E-2}$	$2.1E-{02}_{8.6E-2}$	$0.0E{00}_{0.0E0}$	$5.7E-{02}_{1.0E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$2.2E-{02}_{5.0E-2}$	$0.0E{00}_{0.0E0}$
LIGO-8-76-0.5-1	$2.6E-{02}_{7.7E-2}$	$3.7E-{02}_{1.5E-1}$	$0.0E{00}_{0.0E0}$	$2.3E-{02}_{8.1E-2}$	$3.7E-{04}_{2.0E-3}$	$0.0E{00}_{0.0E0}$	$4.2E-{02}_{1.4E-1}$	$0.0E{00}_{0.0E0}$
LIGO-8-76-0.5-5	$1.7E-{02}_{5.9E-2}$	$5.5E-{02}_{1.3E-1}$	$4.9E-{04}_{2.6E-3}$	$1.7E-{02}_{3.2E-2}$	$0.0E{00}_{0.0E0}$	$4.6E-{03}_{1.8E-2}$	$4.1E-{02}_{1.4E-1}$	$8.5E-{03}_{4.0E-2}$
LIGO-8-76-0.5-10	$1.9E-{04}_{1.0E-3}$	$8.6E-{03}_{3.3E-2}$	$0.0E{00}_{0.0E0}$	$3.9E-{02}_{1.4E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$2.4E-{02}_{6.0E-2}$	$4.4E-{03}_{2.4E-2}$
LIGO-8-76-0.75-0.1	$5.1E-{02}_{1.2E-1}$	$1.6E-{02}_{4.0E-2}$	$1.5E-{03}_{6.9E-3}$	$3.6E-{02}_{5.0E-2}$	$0.0E{00}_{0.0E0}$	$1.7E-{03}_{9.3E-3}$	$3.0E-{02}_{5.9E-2}$	$6.2E-{03}_{1.9E-2}$
LIGO-8-76-0.75-0.5	$1.9E-{02}_{7.1E-2}$	$1.4E-{02}_{5.4E-2}$	$0.0E{00}_{0.0E0}$	$3.6E-{02}_{1.3E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$5.8E-{04}_{3.1E-3}$	$3.2E-{03}_{1.3E-2}$
LIGO-8-76-0.75-1	$0.0E{00}_{0.0E0}$	$1.5E-{02}_{7.9E-2}$	$0.0E{00}_{0.0E0}$	$1.5E-{02}_{6.5E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.4E-{03}_{7.4E-3}$	$0.0E{00}_{0.0E0}$
LIGO-8-76-0.75-5	$5.6E-{02}_{1.5E-1}$	$4.1E-{02}_{8.5E-2}$	$0.0E{00}_{0.0E0}$	$4.1E-{02}_{8.3E-2}$	$0.0E{00}_{0.0E0}$	$9.4E-{04}_{5.0E-3}$	$1.8E-{02}_{5.1E-2}$	$0.0E{00}_{0.0E0}$
LIGO-8-76-0.75-10	$4.1E-{03}_{2.1E-2}$	$0.0E{00}_{0.0E0}$	$1.6E-{02}_{8.4E-2}$	$2.7E-{03}_{1.4E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$6.3E-{03}_{3.4E-2}$	$0.0E{00}_{0.0E0}$
LIGO-8-76-1-0.1	$4.5E-{02}_{6.8E-2}$	$3.7E-{02}_{6.7E-2}$	$1.0E-{03}_{5.4E-3}$	$2.6E-{02}_{5.9E-2}$	$2.2E-{02}_{7.1E-2}$	$1.1E-{02}_{4.0E-2}$	$6.3E-{02}_{9.6E-2}$	$0.0E{00}_{0.0E0}$
LIGO-8-76-1-0.5	$8.9E-{02}_{1.5E-1}$	$1.0E-{01}_{1.4E-1}$	$1.5E-{03}_{8.1E-3}$	$4.8E-{02}_{8.7E-2}$	$2.7E-{02}_{6.9E-2}$	$1.7E-{03}_{8.8E-3}$	$9.1E-{02}_{1.2E-1}$	$2.7E-{02}_{5.0E-2}$
LIGO-8-76-1-1	$3.6E-{02}_{1.0E-1}$	$1.5E-{02}_{3.9E-2}$	$1.2E-{03}_{6.5E-3}$	$2.1E-{02}_{4.6E-2}$	$0.0E{00}_{0.0E0}$	$1.0E-{03}_{5.5E-3}$	$2.5E-{02}_{6.6E-2}$	$1.1E-{02}_{4.0E-2}$
LIGO-8-76-1-5	$4.2E-{02}_{8.5E-2}$	$4.9E-{02}_{1.0E-1}$	$1.5E-{02}_{4.3E-2}$	$5.0E-{02}_{8.7E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$2.1E-{02}_{3.6E-2}$	$1.5E-{03}_{5.2E-3}$
LIGO-8-76-1-10	$0.0E{00}_{0.0E0}$	$3.6E-{02}_{1.4E-1}$	$0.0E{00}_{0.0E0}$	$8.7E-{03}_{3.5E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.4E-{02}_{6.4E-2}$	$0.0E{00}_{0.0E0}$
LIGO-16-76-0.1-0.1	$2.9E-{04}_{1.4E-3}$	$1.5E-{04}_{7.9E-4}$	$0.0E{00}_{0.0E0}$	$3.3E-{02}_{6.2E-2}$	$4.6E-{04}_{2.5E-3}$	$0.0E{00}_{0.0E0}$	$2.5E-{04}_{1.4E-3}$	$0.0E{00}_{0.0E0}$
LIGO-16-76-0.1-0.5	$1.4E-{03}_{5.2E-3}$	$1.7E-{02}_{7.8E-2}$	$0.0E{00}_{0.0E0}$	$9.3E-{02}_{1.7E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$6.7E-{04}_{2.5E-3}$	$0.0E{00}_{0.0E0}$
LIGO-16-76-0.1-1	$6.8E-{02}_{1.1E-1}$	$3.4E-{02}_{6.6E-2}$	$0.0E{00}_{0.0E0}$	$1.8E-{01}_{1.5E-1}$	$1.1E-{03}_{5.7E-3}$	$2.9E-{03}_{1.2E-2}$	$8.5E-{02}_{1.4E-1}$	$1.7E-{02}_{3.4E-2}$
LIGO-16-76-0.1-5	$1.3E-{02}_{6.9E-2}$	$1.3E-{04}_{7.2E-4}$	$0.0E{00}_{0.0E0}$	$4.3E-{02}_{9.5E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.4E-{03}_{7.8E-3}$	$0.0E{00}_{0.0E0}$
LIGO-16-76-0.1-10	$3.8E-{02}_{1.4E-1}$	$2.1E-{02}_{6.0E-2}$	$4.3E-{04}_{2.3E-3}$	$7.7E-{02}_{1.8E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$4.4E-{03}_{2.4E-2}$	$1.0E-{04}_{5.6E-4}$
LIGO-16-76-0.25-0.1	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$3.7E-{02}_{1.2E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
LIGO-16-76-0.25-0.5	$0.0E{00}_{0.0E0}$	$6.6E-{03}_{2.9E-2}$	$0.0E{00}_{0.0E0}$	$3.7E-{02}_{1.4E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
LIGO-16-76-0.25-1	$8.6E-{04}_{4.7E-3}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$3.7E-{02}_{1.7E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
LIGO-16-76-0.25-5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.3E-{02}_{7.2E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
LIGO-16-76-0.25-10	$2.1E-{02}_{6.7E-2}$	$6.6E-{03}_{2.5E-2}$	$0.0E{00}_{0.0E0}$	$7.7E-{02}_{1.8E-1}$	$0.0E{00}_{0.0E0}$	$7.1E-{04}_{3.8E-3}$	$3.1E-{03}_{1.0E-2}$	$0.0E{00}_{0.0E0}$
LIGO-16-76-0.5-0.1	$7.1E-{02}_{1.0E-1}$	$4.3E-{02}_{4.7E-2}$	$8.4E-{04}_{4.5E-3}$	$2.6E-{01}_{2.0E-1}$	$5.8E-{03}_{2.1E-2}$	$1.9E-{03}_{1.0E-2}$	$6.3E-{02}_{1.1E-1}$	$2.3E-{02}_{6.5E-2}$
LIGO-16-76-0.5-0.5	$2.1E-{03}_{1.1E-2}$	$7.4E-{04}_{3.3E-3}$	$0.0E{00}_{0.0E0}$	$8.4E-{02}_{1.3E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$5.4E-{06}_{2.9E-5}$	$0.0E{00}_{0.0E0}$
LIGO-16-76-0.5-1	$2.2E-{02}_{5.1E-2}$	$3.0E-{02}_{7.6E-2}$	$0.0E{00}_{0.0E0}$	$1.8E-{01}_{2.1E-1}$	$9.1E-{04}_{4.9E-3}$	$1.6E-{03}_{6.7E-3}$	$2.6E-{02}_{7.6E-2}$	$3.7E-{03}_{1.5E-2}$
LIGO-16-76-0.5-5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
LIGO-16-76-0.5-10	$6.2E-{03}_{3.3E-2}$	$2.0E-{04}_{1.1E-3}$	$0.0E{00}_{0.0E0}$	$3.3E-{02}_{5.1E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$6.5E-{04}_{3.5E-3}$	$0.0E{00}_{0.0E0}$
LIGO-16-76-0.75-0.1	$1.1E-{02}_{3.7E-2}$	$1.9E-{02}_{5.4E-2}$	$0.0E{00}_{0.0E0}$	$9.2E-{02}_{1.4E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.4E-{02}_{4.7E-2}$	$1.5E-{03}_{8.0E-3}$
LIGO-16-76-0.75-0.5	$0.0E{00}_{0.0E0}$	$4.9E-{03}_{2.6E-2}$	$0.0E{00}_{0.0E0}$	$4.8E-{02}_{1.1E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$9.7E-{03}_{5.2E-2}$	$0.0E{00}_{0.0E0}$
LIGO-16-76-0.75-1	$8.7E-{03}_{3.7E-2}$	$2.0E-{02}_{6.1E-2}$	$0.0E{00}_{0.0E0}$	$4.5E-{02}_{1.1E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.6E-{02}_{7.3E-2}$	$1.9E-{04}_{1.0E-3}$
LIGO-16-76-0.75-5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.9E-{03}_{1.0E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
LIGO-16-76-0.75-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.5E-{02}_{8.0E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
LIGO-16-76-1-0.1	$9.7E-{03}_{3.1E-2}$	$1.8E-{02}_{5.0E-2}$	$0.0E{00}_{0.0E0}$	$6.9E-{02}_{1.2E-1}$	$2.2E-{03}_{1.1E-2}$	$1.3E-{03}_{5.6E-3}$	$3.4E-{02}_{7.4E-2}$	$1.9E-{03}_{9.9E-3}$
LIGO-16-76-1-0.5	$2.3E-{02}_{6.6E-2}$	$2.4E-{02}_{6.6E-2}$	$0.0E{00}_{0.0E0}$	$2.7E-{02}_{6.2E-2}$	$0.0E{00}_{0.0E0}$	$6.9E-{03}_{3.7E-2}$	$6.7E-{02}_{1.6E-1}$	$2.0E-{02}_{5.8E-2}$
LIGO-16-76-1-1	$5.2E-{04}_{2.8E-3}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$7.4E-{03}_{3.2E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
LIGO-16-76-1-5	$1.2E-{02}_{2.9E-2}$	$5.0E-{02}_{1.3E-1}$	$0.0E{00}_{0.0E0}$	$1.0E-{01}_{1.5E-1}$	$0.0E{00}_{0.0E0}$	$2.9E-{03}_{1.6E-2}$	$2.5E-{02}_{5.1E-2}$	$1.0E-{02}_{4.1E-2}$
LIGO-16-76-1-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
LIGO-32-76-0.1-0.1	$1.8E-{03}_{9.6E-3}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$3.4E-{02}_{1.3E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$9.8E-{04}_{5.3E-3}$	$0.0E{00}_{0.0E0}$
LIGO-32-76-0.1-0.5	$8.8E-{02}_{9.4E-2}$	$3.6E-{02}_{6.3E-2}$	$1.3E-{07}_{6.9E-7}$	$2.3E-{01}_{1.9E-1}$	$1.8E-{03}_{6.0E-3}$	$1.6E-{04}_{8.9E-4}$	$7.1E-{02}_{9.6E-2}$	$5.7E-{03}_{2.0E-2}$
LIGO-32-76-0.1-1	$4.4E-{02}_{1.1E-1}$	$8.2E-{03}_{2.4E-2}$	$0.0E{00}_{0.0E0}$	$4.9E-{02}_{7.4E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$5.6E-{03}_{2.2E-2}$	$1.2E-{03}_{6.2E-3}$
LIGO-32-76-0.1-5	$0.0E{00}_{0.0E0}$	$9.9E-{03}_{3.8E-2}$	$0.0E{00}_{0.0E0}$	$3.5E-{02}_{9.7E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$4.0E-{03}_{2.1E-2}$	$0.0E{00}_{0.0E0}$
LIGO-32-76-0.1-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
LIGO-32-76-0.25-0.1	$8.2E-{03}_{1.6E-2}$	$3.9E-{02}_{7.3E-2}$	$0.0E{00}_{0.0E0}$	$1.6E-{01}_{1.4E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.6E-{02}_{4.3E-2}$	$1.4E-{03}_{6.7E-3}$
LIGO-32-76-0.25-0.5	$5.8E-{02}_{8.1E-2}$	$6.9E-{02}_{1.3E-1}$	$1.9E-{03}_{1.0E-2}$	$2.3E-{01}_{1.8E-1}$	$5.9E-{04}_{3.2E-3}$	$2.5E-{04}_{1.4E-3}$	$4.9E-{02}_{6.6E-2}$	$2.1E-{02}_{4.2E-2}$
LIGO-32-76-0.25-1	$1.4E-{02}_{5.5E-2}$	$7.9E-{03}_{2.2E-2}$	$2.0E-{03}_{1.1E-2}$	$1.3E-{01}_{2.4E-1}$	$4.1E-{03}_{2.2E-2}$	$9.9E-{03}_{2.2E-2}$	$2.1E-{02}_{6.5E-2}$	$0.0E{00}_{0.0E0}$
LIGO-32-76-0.25-5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$6.2E-{02}_{1.5E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
LIGO-32-76-0.25-10	$9.7E-{03}_{4.3E-2}$	$1.1E-{02}_{4.6E-2}$	$0.0E{00}_{0.0E0}$	$6.5E-{02}_{1.0E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$4.4E-{02}_{1.6E-1}$	$1.2E-{02}_{4.6E-2}$
LIGO-32-76-0.5-0.1	$6.4E-{02}_{8.4E-2}$	$4.1E-{02}_{8.0E-2}$	$0.0E{00}_{0.0E0}$	$2.0E-{01}_{1.8E-1}$	$1.0E-{03}_{5.5E-3}$	$0.0E{00}_{0.0E0}$	$6.4E-{02}_{8.4E-2}$	$3.5E-{03}_{1.2E-2}$
LIGO-32-76-0.5-0.5	$4.7E-{02}_{1.0E-1}$	$3.0E-{02}_{7.2E-2}$	$0.0E{00}_{0.0E0}$	$1.6E-{01}_{1.3E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$4.8E-{02}_{1.1E-1}$	$7.6E-{03}_{3.4E-2}$
LIGO-32-76-0.5-1	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
LIGO-32-76-0.5-5	$0.0E{00}_{0.0E0}$	$1.1E-{02}_{6.0E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
LIGO-32-76-0.5-10	$0.0E{00}_{0.0E0}$	$3.4E-{05}_{1.8E-4}$	$0.0E{00}_{0.0E0}$	$9.4E-{02}_{1.8E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
LIGO-32-76-0.75-0.1	$3.6E-{03}_{1.4E-2}$	$5.3E-{04}_{2.8E-3}$	$0.0E{00}_{0.0E0}$	$9.3E-{02}_{1.2E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.1E-{02}_{5.1E-2}$	$0.0E{00}_{0.0E0}$
LIGO-32-76-0.75-0.5	$5.3E-{02}_{9.1E-2}$	$3.9E-{02}_{9.3E-2}$	$1.5E-{04}_{8.3E-4}$	$2.7E-{01}_{2.3E-1}$	$1.2E-{04}_{6.3E-4}$	$6.6E-{03}_{3.5E-2}$	$3.9E-{02}_{7.3E-2}$	$4.0E-{03}_{2.0E-2}$
LIGO-32-76-0.75-1	$4.5E-{03}_{1.7E-2}$	$6.4E-{03}_{2.3E-2}$	$8.1E-{05}_{4.4E-4}$	$6.7E-{02}_{1.4E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.7E-{02}_{7.0E-2}$	$0.0E{00}_{0.0E0}$
LIGO-32-76-0.75-5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.9E-{02}_{7.6E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
LIGO-32-76-0.75-10	$5.5E-{04}_{3.0E-3}$	$1.7E-{04}_{8.9E-4}$	$0.0E{00}_{0.0E0}$	$3.2E-{02}_{8.4E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.4E-{03}_{7.5E-3}$	$0.0E{00}_{0.0E0}$
LIGO-32-76-1-0.1	$9.2E-{02}_{8.3E-2}$	$7.9E-{02}_{1.4E-1}$	$1.3E-{02}_{2.9E-2}$	$2.8E-{01}_{1.4E-1}$	$1.6E-{02}_{3.7E-2}$	$4.1E-{02}_{4.5E-2}$	$1.5E-{01}_{1.3E-1}$	$5.7E-{02}_{8.8E-2}$
LIGO-32-76-1-0.5	$1.4E-{01}_{1.6E-1}$	$1.5E-{01}_{1.7E-1}$	$0.0E{00}_{0.0E0}$	$8.7E-{02}_{1.2E-1}$	$6.6E-{03}_{2.3E-2}$	$1.4E-{02}_{7.2E-2}$	$8.1E-{02}_{1.2E-1}$	$1.8E-{02}_{4.2E-2}$
LIGO-32-76-1-1	$3.0E-{02}_{8.1E-2}$	$4.4E-{02}_{1.2E-1}$	$3.2E-{04}_{1.7E-3}$	$1.1E-{01}_{1.3E-1}$	$6.9E-{04}_{3.1E-3}$	$1.5E-{04}_{5.8E-4}$	$5.5E-{02}_{8.7E-2}$	$1.6E-{02}_{5.2E-2}$
LIGO-32-76-1-5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$3.3E-{02}_{1.1E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$4.4E-{03}_{2.3E-2}$	$0.0E{00}_{0.0E0}$
LIGO-32-76-1-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
LIGO-64-76-0.1-0.1	$3.2E-{01}_{1.4E-1}$	$2.9E-{01}_{1.4E-1}$	$2.4E-{01}_{1.3E-1}$	$5.1E-{01}_{1.4E-1}$	$2.1E-{01}_{1.0E-1}$	$3.2E-{01}_{8.4E-2}$	$3.3E-{01}_{1.3E-1}$	$2.2E-{01}_{1.1E-1}$
LIGO-64-76-0.1-0.5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$8.9E-{05}_{4.8E-4}$	$0.0E{00}_{0.0E0}$	$1.6E-{02}_{4.7E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
LIGO-64-76-0.1-1	$5.0E-{06}_{2.7E-5}$	$2.5E-{04}_{1.3E-3}$	$0.0E{00}_{0.0E0}$	$8.1E-{02}_{1.6E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$3.9E-{03}_{1.7E-2}$	$0.0E{00}_{0.0E0}$
LIGO-64-76-0.1-5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.7E-{02}_{9.1E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
LIGO-64-76-0.1-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$4.1E-{02}_{1.4E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
LIGO-64-76-0.25-0.1	$9.3E-{02}_{1.1E-1}$	$8.2E-{02}_{9.1E-2}$	$1.7E-{02}_{6.1E-2}$	$2.4E-{01}_{2.0E-1}$	$1.4E-{02}_{3.7E-2}$	$7.0E-{02}_{8.2E-2}$	$1.1E-{01}_{1.3E-1}$	$1.3E-{02}_{4.3E-2}$
LIGO-64-76-0.25-0.5	$2.4E-{01}_{1.5E-1}$	$2.3E-{01}_{1.5E-1}$	$1.1E-{01}_{1.0E-1}$	$3.9E-{01}_{1.7E-1}$	$1.2E-{01}_{6.3E-2}$	$1.7E-{01}_{6.7E-2}$	$2.7E-{01}_{1.5E-1}$	$1.5E-{01}_{1.1E-1}$
LIGO-64-76-0.25-1	$4.0E-{02}_{6.9E-2}$	$3.8E-{02}_{1.0E-1}$	$0.0E{00}_{0.0E0}$	$6.2E-{02}_{9.8E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$2.3E-{02}_{6.5E-2}$	$8.2E-{03}_{2.5E-2}$
LIGO-64-76-0.25-5	$1.0E-{01}_{1.6E-1}$	$3.2E-{02}_{9.7E-2}$	$1.4E-{04}_{7.4E-4}$	$1.4E-{01}_{2.4E-1}$	$1.6E-{03}_{8.8E-3}$	$0.0E{00}_{0.0E0}$	$3.7E-{02}_{8.8E-2}$	$2.0E-{02}_{7.1E-2}$
LIGO-64-76-0.25-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$3.0E-{02}_{1.4E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
LIGO-64-76-0.5-0.1	$1.2E-{02}_{4.8E-2}$	$2.9E-{02}_{5.2E-2}$	$0.0E{00}_{0.0E0}$	$1.4E-{01}_{1.5E-1}$	$1.1E-{03}_{5.7E-3}$	$0.0E{00}_{0.0E0}$	$3.3E-{02}_{7.5E-2}$	$3.0E-{04}_{1.6E-3}$
LIGO-64-76-0.5-0.5	$1.0E-{01}_{1.2E-1}$	$9.4E-{02}_{1.4E-1}$	$3.0E-{03}_{1.1E-2}$	$1.2E-{01}_{1.3E-1}$	$1.2E-{02}_{3.0E-2}$	$0.0E{00}_{0.0E0}$	$6.2E-{02}_{1.1E-1}$	$3.4E-{02}_{4.9E-2}$
LIGO-64-76-0.5-1	$6.2E-{02}_{1.4E-1}$	$8.7E-{02}_{1.2E-1}$	$7.0E-{03}_{2.7E-2}$	$1.0E-{01}_{1.5E-1}$	$4.7E-{04}_{1.8E-3}$	$0.0E{00}_{0.0E0}$	$4.9E-{02}_{7.7E-2}$	$4.3E-{02}_{8.1E-2}$
LIGO-64-76-0.5-5	$1.1E-{01}_{1.4E-1}$	$8.8E-{02}_{1.2E-1}$	$8.1E-{05}_{4.4E-4}$	$2.1E-{01}_{1.9E-1}$	$9.0E-{04}_{3.4E-3}$	$4.5E-{04}_{2.4E-3}$	$7.6E-{02}_{1.4E-1}$	$5.7E-{02}_{1.2E-1}$
LIGO-64-76-0.5-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
LIGO-64-76-0.75-0.1	$2.3E-{03}_{9.9E-3}$	$7.0E-{03}_{3.2E-2}$	$0.0E{00}_{0.0E0}$	$5.5E-{02}_{1.2E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.2E-{03}_{6.0E-3}$	$8.4E-{03}_{4.5E-2}$
LIGO-64-76-0.75-0.5	$4.0E-{01}_{1.4E-1}$	$4.1E-{01}_{1.7E-1}$	$2.0E-{01}_{1.2E-1}$	$5.4E-{01}_{1.2E-1}$	$2.6E-{01}_{1.0E-1}$	$2.9E-{01}_{1.3E-1}$	$4.3E-{01}_{1.2E-1}$	$2.7E-{01}_{1.1E-1}$
LIGO-64-76-0.75-1	$1.6E-{01}_{1.3E-1}$	$1.4E-{01}_{9.4E-2}$	$3.0E-{02}_{7.7E-2}$	$2.4E-{01}_{1.7E-1}$	$3.5E-{02}_{4.2E-2}$	$3.4E-{02}_{5.9E-2}$	$1.6E-{01}_{1.1E-1}$	$7.9E-{02}_{9.3E-2}$
LIGO-64-76-0.75-5	$3.0E-{02}_{5.6E-2}$	$1.7E-{02}_{5.7E-2}$	$0.0E{00}_{0.0E0}$	$7.4E-{02}_{1.6E-1}$	$1.4E-{02}_{7.5E-2}$	$0.0E{00}_{0.0E0}$	$7.3E-{03}_{3.9E-2}$	$6.9E-{04}_{3.7E-3}$
LIGO-64-76-0.75-10	$4.7E-{03}_{2.5E-2}$	$3.0E-{03}_{1.6E-2}$	$0.0E{00}_{0.0E0}$	$4.0E-{02}_{9.7E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
LIGO-64-76-1-0.1	$2.0E-{01}_{1.4E-1}$	$2.0E-{01}_{1.2E-1}$	$3.6E-{02}_{4.5E-2}$	$3.4E-{01}_{1.1E-1}$	$6.0E-{02}_{6.1E-2}$	$1.0E-{01}_{8.1E-2}$	$1.9E-{01}_{1.1E-1}$	$7.1E-{02}_{8.3E-2}$
LIGO-64-76-1-0.5	$8.1E-{02}_{1.3E-1}$	$8.1E-{02}_{1.2E-1}$	$0.0E{00}_{0.0E0}$	$1.3E-{01}_{1.6E-1}$	$1.5E-{02}_{5.4E-2}$	$0.0E{00}_{0.0E0}$	$6.8E-{02}_{9.9E-2}$	$1.8E-{02}_{4.2E-2}$
LIGO-64-76-1-1	$5.8E-{02}_{1.0E-1}$	$3.7E-{02}_{6.6E-2}$	$3.7E-{04}_{1.6E-3}$	$4.7E-{02}_{6.1E-2}$	$6.2E-{04}_{2.6E-3}$	$0.0E{00}_{0.0E0}$	$1.9E-{02}_{5.3E-2}$	$1.3E-{02}_{3.2E-2}$
LIGO-64-76-1-5	$1.4E-{02}_{3.9E-2}$	$1.5E-{03}_{8.2E-3}$	$0.0E{00}_{0.0E0}$	$4.0E-{02}_{1.1E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.9E-{02}_{5.0E-2}$	$5.8E-{04}_{3.1E-3}$
LIGO-64-76-1-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$2.5E-{02}_{1.3E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$

,

Table 5. Hypervolume mean and standard deviation of the eight algorithms over 30 independent runs. Dark/light gray emphasize the best/second-best results.

Problem	AGEMOEA	AGEMOEA2	GWASFGA	MOCell	MOMBI	MOMBI2	NSGA2	SMS-EMOA
Robot-8-88-0.1-0.1	$2.8E-{03}_{1.5E-2}$	$2.4E-{02}_{9.1E-2}$	$0.0E{00}_{0.0E0}$	$7.7E-{02}_{1.7E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.7E-{02}_{7.6E-2}$	$0.0E{00}_{0.0E0}$
Robot-8-88-0.1-0.5	$1.9E-{02}_{6.0E-2}$	$2.9E-{02}_{7.7E-2}$	$0.0E{00}_{0.0E0}$	$3.4E-{02}_{7.0E-2}$	$9.5E-{04}_{5.1E-3}$	$0.0E{00}_{0.0E0}$	$3.7E-{03}_{1.1E-2}$	$0.0E{00}_{0.0E0}$
Robot-8-88-0.1-1	$5.0E-{02}_{9.1E-2}$	$3.5E-{02}_{5.7E-2}$	$7.4E-{04}_{4.0E-3}$	$1.2E-{01}_{1.7E-1}$	$3.7E-{03}_{1.5E-2}$	$0.0E{00}_{0.0E0}$	$5.8E-{02}_{1.1E-1}$	$2.3E-{02}_{6.3E-2}$
Robot-8-88-0.1-5	$4.6E-{02}_{8.2E-2}$	$2.9E-{02}_{4.7E-2}$	$5.5E-{03}_{1.6E-2}$	$3.7E-{02}_{8.2E-2}$	$4.7E-{03}_{1.6E-2}$	$1.8E-{02}_{6.9E-2}$	$3.7E-{02}_{8.7E-2}$	$1.7E-{02}_{4.1E-2}$
Robot-8-88-0.1-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$4.4E-{03}_{2.4E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$7.3E-{03}_{2.7E-2}$	$0.0E{00}_{0.0E0}$
Robot-8-88-0.25-0.1	$1.9E-{03}_{8.8E-3}$	$5.1E-{03}_{2.7E-2}$	$0.0E{00}_{0.0E0}$	$8.1E-{02}_{1.2E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.1E-{02}_{6.2E-2}$	$0.0E{00}_{0.0E0}$
Robot-8-88-0.25-0.5	$7.9E-{03}_{4.3E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$6.3E-{02}_{1.1E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$2.5E-{03}_{8.5E-3}$	$0.0E{00}_{0.0E0}$
Robot-8-88-0.25-1	$0.0E{00}_{0.0E0}$	$5.7E-{03}_{3.1E-2}$	$0.0E{00}_{0.0E0}$	$9.7E-{03}_{5.3E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$9.8E-{06}_{5.3E-5}$	$0.0E{00}_{0.0E0}$
Robot-8-88-0.25-5	$1.3E-{03}_{5.2E-3}$	$1.1E-{03}_{6.1E-3}$	$0.0E{00}_{0.0E0}$	$7.7E-{04}_{4.1E-3}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.7E-{03}_{9.4E-3}$	$2.9E-{03}_{1.5E-2}$
Robot-8-88-0.25-10	$0.0E{00}_{0.0E0}$	$8.0E-{03}_{4.3E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Robot-8-88-0.5-0.1	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$3.4E-{02}_{9.5E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$3.4E-{03}_{1.8E-2}$	$0.0E{00}_{0.0E0}$
Robot-8-88-0.5-0.5	$4.8E-{03}_{1.9E-2}$	$1.2E-{02}_{3.2E-2}$	$0.0E{00}_{0.0E0}$	$1.3E-{02}_{3.3E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Robot-8-88-0.5-1	$8.1E-{02}_{1.1E-1}$	$9.2E-{02}_{1.6E-1}$	$1.1E-{02}_{4.5E-2}$	$1.4E-{01}_{1.3E-1}$	$2.6E-{02}_{5.4E-2}$	$1.3E-{02}_{2.6E-2}$	$8.7E-{02}_{1.1E-1}$	$2.9E-{02}_{6.2E-2}$
Robot-8-88-0.5-5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Robot-8-88-0.5-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Robot-8-88-0.75-0.1	$5.4E-{03}_{2.1E-2}$	$1.2E-{02}_{3.8E-2}$	$5.8E-{05}_{3.1E-4}$	$7.9E-{02}_{1.6E-1}$	$0.0E{00}_{0.0E0}$	$1.1E-{04}_{4.6E-4}$	$4.0E-{04}_{2.1E-3}$	$0.0E{00}_{0.0E0}$
Robot-8-88-0.75-0.5	$2.9E-{02}_{5.9E-2}$	$2.5E-{02}_{4.3E-2}$	$0.0E{00}_{0.0E0}$	$9.6E-{02}_{1.1E-1}$	$1.2E-{03}_{4.0E-3}$	$9.4E-{03}_{3.2E-2}$	$3.6E-{02}_{5.8E-2}$	$0.0E{00}_{0.0E0}$
Robot-8-88-0.75-1	$2.6E-{02}_{9.7E-2}$	$6.6E-{03}_{2.6E-2}$	$0.0E{00}_{0.0E0}$	$1.1E-{03}_{5.6E-3}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$8.3E-{05}_{4.5E-4}$	$9.5E-{03}_{5.1E-2}$
Robot-8-88-0.75-5	$4.5E-{04}_{2.4E-3}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Robot-8-88-0.75-10	$2.9E-{03}_{1.6E-2}$	$1.7E-{02}_{7.4E-2}$	$0.0E{00}_{0.0E0}$	$3.5E-{03}_{1.6E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.5E-{02}_{5.8E-2}$	$0.0E{00}_{0.0E0}$
Robot-8-88-1-0.1	$2.6E-{02}_{5.5E-2}$	$1.5E-{02}_{5.2E-2}$	$0.0E{00}_{0.0E0}$	$1.5E-{02}_{3.9E-2}$	$2.8E-{04}_{1.5E-3}$	$0.0E{00}_{0.0E0}$	$1.2E-{02}_{3.9E-2}$	$7.1E-{05}_{3.8E-4}$
Robot-8-88-1-0.5	$1.4E-{02}_{4.8E-2}$	$9.0E-{04}_{4.3E-3}$	$0.0E{00}_{0.0E0}$	$1.9E-{03}_{1.0E-2}$	$2.1E-{04}_{1.1E-3}$	$0.0E{00}_{0.0E0}$	$7.9E-{03}_{2.2E-2}$	$0.0E{00}_{0.0E0}$
Robot-8-88-1-1	$9.7E-{03}_{3.1E-2}$	$3.8E-{03}_{2.1E-2}$	$0.0E{00}_{0.0E0}$	$1.7E-{02}_{9.3E-2}$	$2.0E-{03}_{1.1E-2}$	$0.0E{00}_{0.0E0}$	$3.5E-{03}_{1.2E-2}$	$4.8E-{04}_{2.6E-3}$
Robot-8-88-1-5	$1.4E-{02}_{7.5E-2}$	$1.5E-{02}_{5.8E-2}$	$1.2E-{02}_{6.4E-2}$	$1.4E-{03}_{5.8E-3}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.9E-{02}_{1.0E-1}$	$6.3E-{03}_{3.4E-2}$
Robot-8-88-1-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Robot-16-88-0.1-0.1	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.2E-{01}_{1.8E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Robot-16-88-0.1-0.5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$2.8E-{02}_{1.0E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Robot-16-88-0.1-1	$5.0E-{03}_{1.9E-2}$	$1.4E-{02}_{5.2E-2}$	$0.0E{00}_{0.0E0}$	$5.0E-{02}_{1.2E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$8.3E-{03}_{4.4E-2}$	$0.0E{00}_{0.0E0}$
Robot-16-88-0.1-5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$5.2E-{03}_{1.7E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Robot-16-88-0.1-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$6.5E-{02}_{1.9E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$3.5E-{03}_{1.9E-2}$	$0.0E{00}_{0.0E0}$
Robot-16-88-0.25-0.1	$5.7E-{03}_{2.7E-2}$	$1.3E-{03}_{4.6E-3}$	$0.0E{00}_{0.0E0}$	$5.2E-{02}_{1.1E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.3E-{02}_{6.8E-2}$	$0.0E{00}_{0.0E0}$
Robot-16-88-0.25-0.5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$2.3E-{02}_{8.2E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Robot-16-88-0.25-1	$1.2E-{02}_{3.0E-2}$	$4.8E-{03}_{1.4E-2}$	$0.0E{00}_{0.0E0}$	$1.0E-{01}_{1.3E-1}$	$1.5E-{03}_{7.9E-3}$	$6.5E-{03}_{3.4E-2}$	$2.4E-{02}_{8.0E-2}$	$5.0E-{04}_{2.7E-3}$
Robot-16-88-0.25-5	$4.8E-{04}_{2.1E-3}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$3.3E-{02}_{1.1E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Robot-16-88-0.5-0.1	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$7.9E-{02}_{1.1E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$5.9E-{03}_{3.2E-2}$	$1.1E-{03}_{4.9E-3}$
Robot-16-88-0.5-0.5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.4E-{02}_{7.1E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.0E-{02}_{5.4E-2}$	$8.2E-{04}_{4.4E-3}$
Robot-16-88-0.5-1	$4.2E-{02}_{8.0E-2}$	$6.1E-{03}_{1.7E-2}$	$0.0E{00}_{0.0E0}$	$4.8E-{02}_{7.1E-2}$	$0.0E{00}_{0.0E0}$	$2.6E-{03}_{9.6E-3}$	$2.9E-{02}_{5.5E-2}$	$1.5E-{02}_{5.8E-2}$
Robot-16-88-0.5-5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Robot-16-88-0.5-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Robot-16-88-0.75-0.1	$6.9E-{02}_{9.1E-2}$	$9.9E-{02}_{1.1E-1}$	$3.2E-{03}_{8.6E-3}$	$2.9E-{01}_{1.5E-1}$	$1.2E-{02}_{2.5E-2}$	$1.4E-{02}_{2.8E-2}$	$1.0E-{01}_{1.0E-1}$	$2.0E-{02}_{4.6E-2}$
Robot-16-88-0.75-0.5	$1.0E-{02}_{2.8E-2}$	$2.7E-{02}_{7.2E-2}$	$0.0E{00}_{0.0E0}$	$1.3E-{01}_{1.7E-1}$	$0.0E{00}_{0.0E0}$	$1.8E-{04}_{9.5E-4}$	$1.4E-{02}_{4.3E-2}$	$3.6E-{04}_{1.9E-3}$
Robot-16-88-0.75-1	$3.8E-{02}_{1.0E-1}$	$2.9E-{02}_{6.9E-2}$	$0.0E{00}_{0.0E0}$	$1.4E-{01}_{1.6E-1}$	$0.0E{00}_{0.0E0}$	$2.8E-{03}_{1.0E-2}$	$1.8E-{02}_{3.9E-2}$	$1.2E-{02}_{3.2E-2}$
Robot-16-88-0.75-5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$3.7E-{03}_{2.0E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Robot-16-88-0.75-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$3.2E-{02}_{1.1E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Robot-16-88-1-0.1	$1.4E-{02}_{5.1E-2}$	$2.9E-{03}_{9.1E-3}$	$0.0E{00}_{0.0E0}$	$4.6E-{02}_{1.2E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$7.2E-{05}_{3.9E-4}$	$0.0E{00}_{0.0E0}$
Robot-16-88-1-0.5	$4.4E-{04}_{2.4E-3}$	$2.0E-{03}_{1.1E-2}$	$0.0E{00}_{0.0E0}$	$3.0E-{02}_{1.1E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.6E-{03}_{7.8E-3}$	$2.0E-{04}_{1.1E-3}$
Robot-16-88-1-1	$1.0E-{01}_{1.5E-1}$	$5.4E-{02}_{8.4E-2}$	$8.5E-{03}_{3.0E-2}$	$1.8E-{01}_{2.0E-1}$	$2.1E-{02}_{6.5E-2}$	$3.1E-{03}_{1.0E-2}$	$7.9E-{02}_{9.9E-2}$	$4.0E-{02}_{8.4E-2}$
Robot-16-88-1-5	$2.9E-{03}_{1.4E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.0E-{02}_{4.3E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.8E-{03}_{9.6E-3}$	$0.0E{00}_{0.0E0}$
Robot-16-88-1-10	$4.9E-{03}_{2.6E-2}$	$7.7E-{04}_{3.3E-3}$	$0.0E{00}_{0.0E0}$	$6.2E-{02}_{1.6E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$2.9E-{02}_{7.9E-2}$	$1.5E-{05}_{8.2E-5}$
Robot-32-88-0.1-0.1	$4.4E-{02}_{7.2E-2}$	$7.6E-{02}_{1.3E-1}$	$4.2E-{03}_{1.5E-2}$	$3.1E-{01}_{2.3E-1}$	$4.1E-{03}_{1.2E-2}$	$1.2E-{02}_{2.5E-2}$	$1.0E-{01}_{1.0E-1}$	$3.0E-{02}_{5.4E-2}$
Robot-32-88-0.1-0.5	$5.4E-{03}_{2.9E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.0E-{01}_{1.9E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$2.0E-{03}_{7.6E-3}$	$0.0E{00}_{0.0E0}$
Robot-32-88-0.1-1	$1.4E-{03}_{5.5E-3}$	$1.1E-{02}_{3.7E-2}$	$0.0E{00}_{0.0E0}$	$4.3E-{02}_{1.3E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Robot-32-88-0.1-5	$1.1E-{02}_{6.0E-2}$	$3.1E-{04}_{1.7E-3}$	$0.0E{00}_{0.0E0}$	$2.1E-{02}_{6.0E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Robot-32-88-0.1-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$4.3E-{02}_{1.6E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Robot-32-88-0.25-0.1	$1.1E-{01}_{1.2E-1}$	$8.6E-{02}_{6.9E-2}$	$2.6E-{03}_{8.3E-3}$	$2.9E-{01}_{1.8E-1}$	$9.2E-{03}_{3.1E-2}$	$0.0E{00}_{0.0E0}$	$7.8E-{02}_{6.5E-2}$	$2.0E-{02}_{3.5E-2}$
Robot-32-88-0.25-0.5	$1.5E-{04}_{8.1E-4}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$6.6E-{02}_{1.4E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$6.7E-{03}_{3.5E-2}$	$0.0E{00}_{0.0E0}$
Robot-32-88-0.25-1	$0.0E{00}_{0.0E0}$	$9.1E-{04}_{4.9E-3}$	$0.0E{00}_{0.0E0}$	$1.3E-{03}_{4.8E-3}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Robot-32-88-0.25-5	$0.0E{00}_{0.0E0}$	$1.6E-{02}_{6.3E-2}$	$0.0E{00}_{0.0E0}$	$3.1E-{02}_{9.4E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Robot-32-88-0.25-10	$6.9E-{05}_{3.7E-4}$	$2.8E-{04}_{1.5E-3}$	$0.0E{00}_{0.0E0}$	$6.2E-{02}_{1.4E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Robot-32-88-0.5-0.1	$1.6E-{02}_{3.6E-2}$	$2.0E-{02}_{4.0E-2}$	$4.7E-{03}_{2.5E-2}$	$1.7E-{01}_{1.5E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$6.5E-{03}_{1.8E-2}$	$2.9E-{05}_{1.6E-4}$
Robot-32-88-0.5-0.5	$2.4E-{01}_{1.4E-1}$	$1.8E-{01}_{1.6E-1}$	$1.7E-{02}_{3.6E-2}$	$3.5E-{01}_{2.1E-1}$	$3.2E-{02}_{5.5E-2}$	$1.2E-{03}_{6.4E-3}$	$1.9E-{01}_{1.3E-1}$	$1.5E-{01}_{1.3E-1}$
Robot-32-88-0.5-1	$9.9E-{03}_{5.3E-2}$	$1.9E-{03}_{1.0E-2}$	$0.0E{00}_{0.0E0}$	$1.9E-{02}_{1.0E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.0E-{02}_{5.5E-2}$	$0.0E{00}_{0.0E0}$
Robot-32-88-0.5-5	$3.5E-{03}_{1.7E-2}$	$3.0E-{02}_{1.0E-1}$	$0.0E{00}_{0.0E0}$	$7.2E-{02}_{1.4E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$3.0E-{03}_{9.5E-3}$	$0.0E{00}_{0.0E0}$
Robot-32-88-0.5-10	$9.3E-{05}_{5.0E-4}$	$5.2E-{04}_{2.8E-3}$	$0.0E{00}_{0.0E0}$	$8.1E-{02}_{1.6E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.2E-{02}_{5.0E-2}$	$0.0E{00}_{0.0E0}$
Robot-32-88-0.75-0.1	$3.8E-{02}_{6.1E-2}$	$1.7E-{02}_{5.4E-2}$	$0.0E{00}_{0.0E0}$	$1.7E-{01}_{1.5E-1}$	$1.9E-{03}_{1.0E-2}$	$7.7E-{03}_{3.5E-2}$	$2.2E-{02}_{5.1E-2}$	$2.6E-{03}_{9.4E-3}$
Robot-32-88-0.75-0.5	$5.1E-{04}_{2.8E-3}$	$3.6E-{03}_{1.5E-2}$	$0.0E{00}_{0.0E0}$	$8.5E-{02}_{1.6E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$5.9E-{03}_{1.7E-2}$	$0.0E{00}_{0.0E0}$
Robot-32-88-0.75-1	$3.4E-{02}_{1.0E-1}$	$2.3E-{02}_{6.3E-2}$	$4.4E-{04}_{2.4E-3}$	$8.1E-{02}_{1.3E-1}$	$2.8E-{04}_{1.5E-3}$	$0.0E{00}_{0.0E0}$	$1.0E-{02}_{2.4E-2}$	$4.0E-{04}_{1.3E-3}$
Robot-32-88-0.75-5	$3.5E-{03}_{1.9E-2}$	$1.2E-{02}_{6.6E-2}$	$0.0E{00}_{0.0E0}$	$4.4E-{02}_{1.4E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$8.8E-{04}_{3.5E-3}$	$0.0E{00}_{0.0E0}$
Robot-32-88-0.75-10	$2.0E-{02}_{7.5E-2}$	$3.6E-{03}_{1.9E-2}$	$9.7E-{03}_{5.2E-2}$	$1.5E-{01}_{2.3E-1}$	$0.0E{00}_{0.0E0}$	$7.9E-{04}_{4.3E-3}$	$5.5E-{03}_{2.9E-2}$	$0.0E{00}_{0.0E0}$
Robot-32-88-1-0.1	$8.6E-{02}_{1.1E-1}$	$1.1E-{01}_{1.3E-1}$	$2.8E-{03}_{1.4E-2}$	$2.2E-{01}_{1.7E-1}$	$1.2E-{02}_{3.5E-2}$	$1.2E-{02}_{5.5E-2}$	$1.2E-{01}_{1.3E-1}$	$4.5E-{02}_{6.3E-2}$
Robot-32-88-1-0.5	$1.3E-{02}_{3.1E-2}$	$2.4E-{02}_{8.6E-2}$	$0.0E{00}_{0.0E0}$	$5.1E-{02}_{9.4E-2}$	$5.3E-{03}_{2.4E-2}$	$0.0E{00}_{0.0E0}$	$4.2E-{02}_{1.3E-1}$	$6.9E-{03}_{2.3E-2}$
Robot-32-88-1-1	$1.8E-{02}_{3.6E-2}$	$8.0E-{03}_{3.3E-2}$	$0.0E{00}_{0.0E0}$	$1.3E-{01}_{2.0E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$3.1E-{03}_{1.4E-2}$	$3.1E-{03}_{1.7E-2}$
Robot-32-88-1-5	$0.0E{00}_{0.0E0}$	$3.4E-{03}_{1.8E-2}$	$0.0E{00}_{0.0E0}$	$3.9E-{02}_{8.4E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Robot-32-88-1-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Robot-64-88-0.1-0.1	$3.4E-{01}_{1.2E-1}$	$4.3E-{01}_{1.3E-1}$	$2.3E-{01}_{8.7E-2}$	$5.4E-{01}_{1.2E-1}$	$2.1E-{01}_{8.8E-2}$	$2.2E-{01}_{9.8E-2}$	$4.3E-{01}_{1.3E-1}$	$2.6E-{01}_{9.2E-2}$
Robot-64-88-0.1-0.5	$1.7E-{02}_{5.2E-2}$	$3.1E-{03}_{1.7E-2}$	$0.0E{00}_{0.0E0}$	$3.0E-{02}_{9.1E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$7.0E-{03}_{3.8E-2}$
Robot-64-88-0.1-1	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$9.3E-{03}_{5.0E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Robot-64-88-0.1-5	$5.1E-{02}_{7.3E-2}$	$5.4E-{02}_{1.0E-1}$	$3.2E-{03}_{1.5E-2}$	$1.9E-{01}_{1.7E-1}$	$6.8E-{03}_{2.7E-2}$	$0.0E{00}_{0.0E0}$	$6.4E-{02}_{9.9E-2}$	$2.6E-{02}_{5.9E-2}$
Robot-64-88-0.1-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$3.3E-{02}_{1.3E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.9E-{03}_{1.0E-2}$	$0.0E{00}_{0.0E0}$
Robot-64-88-0.25-0.1	$3.4E-{01}_{1.2E-1}$	$3.4E-{01}_{1.2E-1}$	$2.0E-{01}_{1.0E-1}$	$4.7E-{01}_{1.1E-1}$	$1.7E-{01}_{6.1E-2}$	$1.1E-{01}_{1.1E-1}$	$3.6E-{01}_{8.5E-2}$	$2.5E-{01}_{1.1E-1}$
Robot-64-88-0.25-0.5	$4.8E-{01}_{1.1E-1}$	$4.6E-{01}_{1.6E-1}$	$3.2E-{01}_{1.2E-1}$	$5.7E-{01}_{8.6E-2}$	$3.1E-{01}_{1.4E-1}$	$3.1E-{01}_{8.2E-2}$	$4.5E-{01}_{1.1E-1}$	$3.7E-{01}_{9.2E-2}$
Robot-64-88-0.25-1	$4.1E-{01}_{1.4E-1}$	$3.9E-{01}_{1.2E-1}$	$2.2E-{01}_{8.8E-2}$	$5.9E-{01}_{1.2E-1}$	$2.2E-{01}_{1.0E-1}$	$1.8E-{01}_{1.2E-1}$	$3.9E-{01}_{1.2E-1}$	$2.8E-{01}_{1.1E-1}$
Robot-64-88-0.25-5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$4.1E-{03}_{2.2E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.3E-{02}_{6.9E-2}$	$0.0E{00}_{0.0E0}$
Robot-64-88-0.25-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.5E-{02}_{8.3E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Robot-64-88-0.5-0.1	$3.1E-{01}_{1.1E-1}$	$3.3E-{01}_{1.4E-1}$	$2.4E-{01}_{1.1E-1}$	$5.4E-{01}_{1.2E-1}$	$2.3E-{01}_{1.3E-1}$	$2.9E-{01}_{1.1E-1}$	$3.9E-{01}_{1.5E-1}$	$2.5E-{01}_{1.2E-1}$
Robot-64-88-0.5-0.5	$4.2E-{02}_{1.2E-1}$	$7.6E-{03}_{3.4E-2}$	$0.0E{00}_{0.0E0}$	$3.6E-{02}_{9.2E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$3.7E-{03}_{1.2E-2}$	$6.0E-{03}_{2.9E-2}$
Robot-64-88-0.5-1	$2.0E-{01}_{1.7E-1}$	$2.0E-{01}_{1.5E-1}$	$3.5E-{02}_{7.3E-2}$	$3.0E-{01}_{1.8E-1}$	$3.7E-{02}_{8.7E-2}$	$5.9E-{03}_{3.2E-2}$	$1.3E-{01}_{1.1E-1}$	$1.7E-{01}_{1.7E-1}$
Robot-64-88-0.5-5	$5.8E-{03}_{2.5E-2}$	$2.3E-{04}_{1.2E-3}$	$0.0E{00}_{0.0E0}$	$1.2E-{01}_{2.3E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$8.4E-{06}_{4.5E-5}$	$0.0E{00}_{0.0E0}$
Robot-64-88-0.5-10	$2.5E-{02}_{1.3E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$8.8E-{03}_{2.7E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Robot-64-88-0.75-0.1	$3.2E-{01}_{1.2E-1}$	$3.4E-{01}_{1.0E-1}$	$1.4E-{01}_{1.0E-1}$	$5.2E-{01}_{1.3E-1}$	$1.6E-{01}_{8.1E-2}$	$9.1E-{02}_{8.4E-2}$	$3.7E-{01}_{1.5E-1}$	$2.0E-{01}_{1.1E-1}$
Robot-64-88-0.75-0.5	$2.6E-{01}_{1.5E-1}$	$3.2E-{01}_{1.6E-1}$	$1.0E-{01}_{9.8E-2}$	$3.9E-{01}_{1.6E-1}$	$1.0E-{01}_{7.8E-2}$	$7.0E-{02}_{8.9E-2}$	$2.7E-{01}_{1.5E-1}$	$1.8E-{01}_{1.3E-1}$
Robot-64-88-0.75-1	$1.2E-{02}_{3.8E-2}$	$2.5E-{02}_{1.3E-1}$	$0.0E{00}_{0.0E0}$	$7.8E-{02}_{2.0E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$3.1E-{02}_{1.1E-1}$	$0.0E{00}_{0.0E0}$
Robot-64-88-0.75-5	$9.1E-{02}_{1.1E-1}$	$1.2E-{01}_{1.2E-1}$	$1.7E-{02}_{2.8E-2}$	$3.1E-{01}_{1.7E-1}$	$1.5E-{02}_{2.6E-2}$	$3.1E-{02}_{7.6E-2}$	$1.6E-{01}_{1.4E-1}$	$8.5E-{02}_{1.0E-1}$
Robot-64-88-0.75-10	$8.6E-{03}_{4.2E-2}$	$2.8E-{02}_{1.4E-1}$	$0.0E{00}_{0.0E0}$	$5.4E-{02}_{1.4E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$2.8E-{03}_{1.4E-2}$	$0.0E{00}_{0.0E0}$
Robot-64-88-1-0.1	$2.4E-{02}_{6.5E-2}$	$2.1E-{02}_{4.8E-2}$	$0.0E{00}_{0.0E0}$	$1.2E-{01}_{1.5E-1}$	$1.2E-{03}_{6.3E-3}$	$2.5E-{03}_{9.6E-3}$	$3.0E-{02}_{6.6E-2}$	$8.0E-{04}_{4.3E-3}$
Robot-64-88-1-0.5	$1.3E-{01}_{1.9E-1}$	$1.0E-{01}_{1.2E-1}$	$4.9E-{04}_{2.6E-3}$	$1.2E-{01}_{1.4E-1}$	$9.2E-{04}_{2.8E-3}$	$0.0E{00}_{0.0E0}$	$8.4E-{02}_{1.6E-1}$	$2.2E-{02}_{4.3E-2}$
Robot-64-88-1-1	$2.0E-{03}_{7.4E-3}$	$2.9E-{03}_{1.0E-2}$	$0.0E{00}_{0.0E0}$	$2.6E-{02}_{8.6E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$9.1E-{04}_{4.9E-3}$	$3.0E-{03}_{1.6E-2}$
Robot-64-88-1-5	$1.8E-{01}_{1.4E-1}$	$1.1E-{01}_{9.7E-2}$	$4.1E-{02}_{4.7E-2}$	$3.6E-{01}_{1.8E-1}$	$2.2E-{02}_{4.2E-2}$	$5.8E-{02}_{8.6E-2}$	$1.7E-{01}_{1.4E-1}$	$7.1E-{02}_{8.5E-2}$
Robot-64-88-1-10	$2.8E-{02}_{9.5E-2}$	$1.1E-{02}_{5.8E-2}$	$0.0E{00}_{0.0E0}$	$3.2E-{02}_{9.7E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$5.9E-{03}_{2.1E-2}$	$0.0E{00}_{0.0E0}$

and

Table 6. Hypervolume mean and standard deviation of the eight algorithms over 30 independent runs. Dark/light gray emphasize the best/second-best results.

Problem	AGEMOEA	AGEMOEA2	GWASFGA	MOCell	MOMBI	MOMBI2	NSGA2	SMS-EMOA
Sparse-8-96-0.1-0.1	$8.7E-{02}_{9.1E-2}$	$7.8E-{02}_{7.7E-2}$	$2.3E-{03}_{9.3E-3}$	$1.9E-{01}_{1.7E-1}$	$1.4E-{02}_{3.3E-2}$	$1.9E-{02}_{4.0E-2}$	$1.0E-{01}_{1.0E-1}$	$2.7E-{02}_{4.0E-2}$
Sparse-8-96-0.1-0.5	$2.5E-{01}_{1.2E-1}$	$2.7E-{01}_{1.2E-1}$	$3.0E-{02}_{4.2E-2}$	$2.9E-{01}_{1.5E-1}$	$9.2E-{02}_{8.1E-2}$	$8.5E-{02}_{9.1E-2}$	$2.4E-{01}_{1.3E-1}$	$1.0E-{01}_{9.0E-2}$
Sparse-8-96-0.1-1	$1.0E-{01}_{9.0E-2}$	$1.3E-{01}_{1.0E-1}$	$3.8E-{02}_{5.1E-2}$	$2.2E-{01}_{1.3E-1}$	$4.6E-{02}_{5.3E-2}$	$6.0E-{02}_{6.1E-2}$	$1.4E-{01}_{1.1E-1}$	$4.6E-{02}_{5.2E-2}$
Sparse-8-96-0.1-5	$5.1E-{02}_{7.4E-2}$	$8.4E-{02}_{1.2E-1}$	$5.6E-{03}_{2.0E-2}$	$1.3E-{01}_{1.6E-1}$	$2.0E-{03}_{7.5E-3}$	$1.0E-{03}_{5.3E-3}$	$6.4E-{02}_{1.3E-1}$	$1.2E-{02}_{3.2E-2}$
Sparse-8-96-0.1-10	$7.8E-{02}_{1.2E-1}$	$5.7E-{02}_{1.0E-1}$	$4.0E-{03}_{1.7E-2}$	$1.8E-{01}_{2.0E-1}$	$2.1E-{03}_{1.1E-2}$	$0.0E{00}_{0.0E0}$	$5.1E-{02}_{1.2E-1}$	$2.9E-{03}_{1.5E-2}$
Sparse-8-96-0.25-0.1	$2.3E-{01}_{1.0E-1}$	$2.4E-{01}_{1.4E-1}$	$8.6E-{02}_{8.3E-2}$	$3.3E-{01}_{1.7E-1}$	$8.1E-{02}_{6.6E-2}$	$7.9E-{02}_{9.2E-2}$	$2.7E-{01}_{1.2E-1}$	$9.9E-{02}_{7.0E-2}$
Sparse-8-96-0.25-0.5	$1.1E-{02}_{2.6E-2}$	$1.1E-{02}_{3.9E-2}$	$1.9E-{03}_{1.0E-2}$	$3.5E-{02}_{6.8E-2}$	$0.0E{00}_{0.0E0}$	$5.1E-{05}_{2.8E-4}$	$6.4E-{03}_{3.5E-2}$	$0.0E{00}_{0.0E0}$
Sparse-8-96-0.25-1	$3.8E-{01}_{1.2E-1}$	$3.8E-{01}_{1.5E-1}$	$1.9E-{01}_{8.5E-2}$	$4.9E-{01}_{1.1E-1}$	$1.8E-{01}_{1.2E-1}$	$2.3E-{01}_{1.1E-1}$	$3.8E-{01}_{1.2E-1}$	$2.1E-{01}_{1.1E-1}$
Sparse-8-96-0.25-5	$4.7E-{02}_{1.3E-1}$	$1.7E-{02}_{4.1E-2}$	$2.7E-{03}_{8.7E-3}$	$6.6E-{02}_{1.4E-1}$	$3.8E-{04}_{2.0E-3}$	$2.0E-{04}_{8.3E-4}$	$7.4E-{02}_{1.6E-1}$	$1.3E-{03}_{6.6E-3}$
Sparse-8-96-0.25-10	$0.0E{00}_{0.0E0}$	$2.1E-{04}_{1.1E-3}$	$0.0E{00}_{0.0E0}$	$1.4E-{02}_{4.6E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.5E-{02}_{6.5E-2}$	$6.3E-{03}_{3.4E-2}$
Sparse-8-96-0.5-0.1	$1.8E-{01}_{1.7E-1}$	$1.4E-{01}_{1.3E-1}$	$1.4E-{02}_{6.6E-2}$	$2.0E-{01}_{1.7E-1}$	$9.0E-{02}_{1.1E-1}$	$1.3E-{01}_{1.5E-1}$	$1.9E-{01}_{1.6E-1}$	$7.3E-{02}_{9.2E-2}$
Sparse-8-96-0.5-0.5	$1.2E-{02}_{2.8E-2}$	$4.4E-{03}_{2.3E-2}$	$0.0E{00}_{0.0E0}$	$2.0E-{02}_{5.5E-2}$	$0.0E{00}_{0.0E0}$	$8.6E-{04}_{4.6E-3}$	$1.8E-{02}_{5.2E-2}$	$4.2E-{03}_{2.3E-2}$
Sparse-8-96-0.5-1	$2.9E-{02}_{7.3E-2}$	$1.5E-{02}_{5.6E-2}$	$0.0E{00}_{0.0E0}$	$5.6E-{02}_{1.2E-1}$	$1.6E-{03}_{8.4E-3}$	$1.2E-{02}_{4.3E-2}$	$1.6E-{02}_{5.3E-2}$	$8.3E-{04}_{3.2E-3}$
Sparse-8-96-0.5-5	$7.3E-{02}_{1.2E-1}$	$9.9E-{02}_{1.2E-1}$	$1.0E-{02}_{5.3E-2}$	$1.3E-{01}_{1.7E-1}$	$1.3E-{02}_{3.2E-2}$	$1.9E-{02}_{9.0E-2}$	$1.0E-{01}_{1.2E-1}$	$3.1E-{02}_{7.6E-2}$
Sparse-8-96-0.5-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$4.0E-{03}_{2.2E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Sparse-8-96-0.75-0.1	$4.7E-{02}_{1.1E-1}$	$3.2E-{02}_{7.8E-2}$	$1.2E-{02}_{6.6E-2}$	$1.6E-{02}_{4.3E-2}$	$0.0E{00}_{0.0E0}$	$2.3E-{03}_{1.1E-2}$	$3.7E-{02}_{1.1E-1}$	$2.3E-{02}_{5.7E-2}$
Sparse-8-96-0.75-0.5	$6.1E-{02}_{9.3E-2}$	$8.0E-{02}_{1.5E-1}$	$8.5E-{04}_{3.2E-3}$	$5.6E-{02}_{8.3E-2}$	$2.5E-{03}_{1.0E-2}$	$4.4E-{03}_{1.0E-2}$	$8.7E-{02}_{1.6E-1}$	$7.3E-{03}_{2.1E-2}$
Sparse-8-96-0.75-1	$3.2E-{02}_{1.1E-1}$	$2.2E-{02}_{5.8E-2}$	$0.0E{00}_{0.0E0}$	$2.3E-{02}_{9.5E-2}$	$0.0E{00}_{0.0E0}$	$3.3E-{03}_{1.8E-2}$	$2.4E-{02}_{7.6E-2}$	$6.4E-{03}_{3.0E-2}$
Sparse-8-96-0.75-5	$0.0E{00}_{0.0E0}$	$7.3E-{03}_{3.9E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.2E-{02}_{6.7E-2}$	$0.0E{00}_{0.0E0}$
Sparse-8-96-0.75-10	$4.4E-{02}_{1.1E-1}$	$2.9E-{02}_{7.6E-2}$	$1.4E-{02}_{7.1E-2}$	$6.7E-{02}_{1.5E-1}$	$0.0E{00}_{0.0E0}$	$3.3E-{03}_{1.8E-2}$	$7.2E-{02}_{1.5E-1}$	$1.1E-{02}_{5.0E-2}$
Sparse-8-96-1-0.1	$9.2E-{03}_{4.0E-2}$	$3.0E-{03}_{1.2E-2}$	$0.0E{00}_{0.0E0}$	$8.4E-{03}_{4.5E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.1E-{02}_{4.3E-2}$	$0.0E{00}_{0.0E0}$
Sparse-8-96-1-0.5	$3.4E-{02}_{8.4E-2}$	$6.8E-{02}_{1.4E-1}$	$2.2E-{02}_{5.1E-2}$	$1.1E-{02}_{4.4E-2}$	$6.4E-{03}_{2.2E-2}$	$2.9E-{03}_{1.1E-2}$	$3.5E-{02}_{6.3E-2}$	$9.6E-{03}_{2.6E-2}$
Sparse-8-96-1-1	$5.4E-{02}_{1.2E-1}$	$4.2E-{02}_{8.3E-2}$	$1.2E-{02}_{4.2E-2}$	$1.6E-{02}_{5.6E-2}$	$3.6E-{03}_{1.4E-2}$	$6.3E-{03}_{2.9E-2}$	$2.3E-{02}_{4.4E-2}$	$5.3E-{03}_{1.8E-2}$
Sparse-8-96-1-5	$8.4E-{02}_{1.5E-1}$	$9.2E-{02}_{1.3E-1}$	$4.4E-{03}_{1.3E-2}$	$7.9E-{02}_{1.7E-1}$	$1.2E-{03}_{3.9E-3}$	$9.3E-{03}_{2.6E-2}$	$5.1E-{02}_{9.5E-2}$	$1.4E-{02}_{4.9E-2}$
Sparse-8-96-1-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$8.2E-{04}_{4.4E-3}$	$0.0E{00}_{0.0E0}$
Sparse-16-96-0.1-0.1	$2.2E-{01}_{1.3E-1}$	$2.0E-{01}_{1.1E-1}$	$4.5E-{02}_{5.2E-2}$	$4.0E-{01}_{9.3E-2}$	$7.7E-{02}_{9.1E-2}$	$9.0E-{02}_{7.7E-2}$	$2.1E-{01}_{1.0E-1}$	$7.2E-{02}_{8.0E-2}$
Sparse-16-96-0.1-0.5	$2.0E-{02}_{5.1E-2}$	$1.4E-{02}_{6.0E-2}$	$0.0E{00}_{0.0E0}$	$1.3E-{01}_{1.6E-1}$	$7.1E-{03}_{3.8E-2}$	$1.9E-{03}_{9.1E-3}$	$1.8E-{02}_{3.6E-2}$	$1.0E-{04}_{5.4E-4}$
Sparse-16-96-0.1-1	$1.5E-{02}_{4.2E-2}$	$1.4E-{02}_{5.0E-2}$	$0.0E{00}_{0.0E0}$	$8.1E-{02}_{1.4E-1}$	$2.3E-{03}_{1.2E-2}$	$0.0E{00}_{0.0E0}$	$1.4E-{02}_{2.5E-2}$	$3.5E-{05}_{1.9E-4}$
Sparse-16-96-0.1-5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$6.4E-{03}_{3.4E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Sparse-16-96-0.1-10	$1.3E-{03}_{4.9E-3}$	$2.1E-{02}_{7.6E-2}$	$0.0E{00}_{0.0E0}$	$1.1E-{01}_{2.2E-1}$	$0.0E{00}_{0.0E0}$	$7.1E-{04}_{2.7E-3}$	$5.6E-{02}_{1.7E-1}$	$2.8E-{02}_{1.0E-1}$
Sparse-16-96-0.25-0.1	$1.0E-{03}_{4.2E-3}$	$8.6E-{04}_{4.0E-3}$	$0.0E{00}_{0.0E0}$	$4.0E-{02}_{6.5E-2}$	$0.0E{00}_{0.0E0}$	$1.4E-{04}_{7.6E-4}$	$8.3E-{03}_{2.5E-2}$	$0.0E{00}_{0.0E0}$
Sparse-16-96-0.25-0.5	$4.9E-{04}_{2.6E-3}$	$7.7E-{03}_{2.9E-2}$	$0.0E{00}_{0.0E0}$	$2.8E-{02}_{7.6E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Sparse-16-96-0.25-1	$3.1E-{02}_{9.0E-2}$	$1.5E-{02}_{4.5E-2}$	$0.0E{00}_{0.0E0}$	$7.9E-{02}_{1.2E-1}$	$6.9E-{04}_{3.7E-3}$	$0.0E{00}_{0.0E0}$	$6.0E-{03}_{1.8E-2}$	$2.0E-{03}_{8.4E-3}$
Sparse-16-96-0.25-5	$8.8E-{02}_{1.2E-1}$	$1.1E-{01}_{1.5E-1}$	$2.7E-{03}_{1.5E-2}$	$1.7E-{01}_{1.6E-1}$	$7.0E-{03}_{3.2E-2}$	$1.3E-{02}_{4.0E-2}$	$7.2E-{02}_{1.4E-1}$	$1.0E-{02}_{2.9E-2}$
Sparse-16-96-0.25-10	$4.2E-{03}_{1.3E-2}$	$5.6E-{03}_{1.8E-2}$	$0.0E{00}_{0.0E0}$	$4.3E-{02}_{6.0E-2}$	$2.5E-{05}_{1.3E-4}$	$0.0E{00}_{0.0E0}$	$1.7E-{02}_{7.7E-2}$	$3.3E-{05}_{1.8E-4}$
Sparse-16-96-0.5-0.1	$7.8E-{02}_{1.1E-1}$	$7.0E-{02}_{9.6E-2}$	$4.7E-{04}_{1.9E-3}$	$1.6E-{01}_{1.3E-1}$	$1.3E-{02}_{2.9E-2}$	$5.7E-{03}_{1.5E-2}$	$6.9E-{02}_{8.7E-2}$	$1.7E-{02}_{3.6E-2}$
Sparse-16-96-0.5-0.5	$8.2E-{02}_{9.3E-2}$	$5.9E-{02}_{8.7E-2}$	$5.9E-{03}_{1.7E-2}$	$2.1E-{01}_{1.8E-1}$	$6.7E-{03}_{2.1E-2}$	$4.3E-{03}_{1.6E-2}$	$5.8E-{02}_{6.7E-2}$	$8.7E-{03}_{2.6E-2}$
Sparse-16-96-0.5-1	$9.9E-{03}_{3.8E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$2.3E-{02}_{7.2E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Sparse-16-96-0.5-5	$2.2E-{02}_{7.4E-2}$	$2.0E-{02}_{7.8E-2}$	$0.0E{00}_{0.0E0}$	$8.1E-{02}_{1.7E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$3.5E-{03}_{1.3E-2}$	$2.2E-{03}_{8.4E-3}$
Sparse-16-96-0.5-10	$0.0E{00}_{0.0E0}$	$3.3E-{03}_{1.2E-2}$	$0.0E{00}_{0.0E0}$	$2.1E-{02}_{5.5E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.9E-{02}_{8.7E-2}$	$1.4E-{02}_{7.6E-2}$
Sparse-16-96-0.75-0.1	$0.0E{00}_{0.0E0}$	$2.4E-{03}_{9.0E-3}$	$0.0E{00}_{0.0E0}$	$3.8E-{02}_{9.4E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Sparse-16-96-0.75-0.5	$1.3E-{01}_{1.3E-1}$	$1.0E-{01}_{1.0E-1}$	$1.7E-{02}_{4.6E-2}$	$1.9E-{01}_{1.4E-1}$	$4.2E-{02}_{8.2E-2}$	$4.7E-{02}_{6.6E-2}$	$1.5E-{01}_{1.2E-1}$	$1.9E-{02}_{4.3E-2}$
Sparse-16-96-0.75-1	$1.0E-{02}_{3.6E-2}$	$5.0E-{03}_{1.7E-2}$	$0.0E{00}_{0.0E0}$	$3.6E-{02}_{7.7E-2}$	$0.0E{00}_{0.0E0}$	$3.3E-{04}_{1.8E-3}$	$1.3E-{02}_{4.0E-2}$	$2.5E-{03}_{1.4E-2}$
Sparse-16-96-0.75-5	$2.8E-{02}_{1.0E-1}$	$1.6E-{02}_{5.8E-2}$	$0.0E{00}_{0.0E0}$	$6.4E-{02}_{1.1E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.0E-{02}_{4.2E-2}$	$3.1E-{03}_{1.7E-2}$
Sparse-16-96-0.75-10	$8.5E-{02}_{1.5E-1}$	$2.5E-{02}_{6.4E-2}$	$6.2E-{05}_{3.3E-4}$	$6.4E-{02}_{1.8E-1}$	$5.0E-{03}_{2.7E-2}$	$1.3E-{03}_{6.8E-3}$	$2.3E-{02}_{9.0E-2}$	$5.9E-{03}_{2.6E-2}$
Sparse-16-96-1-0.1	$1.7E-{03}_{9.4E-3}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$2.7E-{03}_{1.4E-2}$	$0.0E{00}_{0.0E0}$	$4.8E-{03}_{2.6E-2}$	$3.0E-{03}_{9.5E-3}$	$1.3E-{03}_{7.2E-3}$
Sparse-16-96-1-0.5	$0.0E{00}_{0.0E0}$	$2.9E-{03}_{1.6E-2}$	$0.0E{00}_{0.0E0}$	$4.5E-{03}_{2.4E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$6.0E-{03}_{3.1E-2}$	$0.0E{00}_{0.0E0}$
Sparse-16-96-1-1	$1.3E-{02}_{6.9E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$9.8E-{04}_{5.3E-3}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Sparse-16-96-1-5	$6.6E-{03}_{3.5E-2}$	$2.6E-{02}_{1.1E-1}$	$0.0E{00}_{0.0E0}$	$5.8E-{02}_{9.8E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$2.6E-{02}_{1.1E-1}$	$3.3E-{03}_{1.8E-2}$
Sparse-16-96-1-10	$8.0E-{02}_{1.8E-1}$	$3.0E-{02}_{9.5E-2}$	$8.2E-{04}_{3.1E-3}$	$1.1E-{02}_{4.8E-2}$	$1.0E-{05}_{5.6E-5}$	$7.1E-{03}_{2.7E-2}$	$3.1E-{02}_{9.4E-2}$	$8.8E-{03}_{2.6E-2}$
Sparse-32-96-0.1-0.1	$1.5E-{01}_{1.0E-1}$	$1.5E-{01}_{1.4E-1}$	$9.9E-{03}_{2.3E-2}$	$3.4E-{01}_{2.0E-1}$	$1.3E-{02}_{2.3E-2}$	$1.1E-{02}_{2.9E-2}$	$1.1E-{01}_{1.1E-1}$	$4.0E-{02}_{7.6E-2}$
Sparse-32-96-0.1-0.5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$8.5E-{02}_{2.1E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Sparse-32-96-0.1-1	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$6.2E-{02}_{1.2E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Sparse-32-96-0.1-5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$6.1E-{03}_{2.3E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$2.1E-{04}_{1.1E-3}$	$0.0E{00}_{0.0E0}$
Sparse-32-96-0.1-10	$3.9E-{02}_{9.2E-2}$	$3.7E-{03}_{2.0E-2}$	$0.0E{00}_{0.0E0}$	$6.3E-{02}_{1.6E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$2.2E-{02}_{8.6E-2}$	$0.0E{00}_{0.0E0}$
Sparse-32-96-0.25-0.1	$1.2E-{02}_{5.0E-2}$	$6.2E-{03}_{3.3E-2}$	$0.0E{00}_{0.0E0}$	$8.9E-{02}_{1.3E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$8.1E-{03}_{3.0E-2}$	$1.5E-{04}_{7.9E-4}$
Sparse-32-96-0.25-0.5	$1.9E-{02}_{5.1E-2}$	$2.4E-{02}_{6.4E-2}$	$0.0E{00}_{0.0E0}$	$8.9E-{02}_{1.5E-1}$	$2.0E-{03}_{1.1E-2}$	$1.1E-{02}_{4.2E-2}$	$3.0E-{02}_{6.6E-2}$	$2.0E-{02}_{5.2E-2}$
Sparse-32-96-0.25-1	$5.0E-{05}_{2.7E-4}$	$3.9E-{04}_{1.5E-3}$	$0.0E{00}_{0.0E0}$	$1.7E-{03}_{6.9E-3}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$8.6E-{03}_{4.6E-2}$	$0.0E{00}_{0.0E0}$
Sparse-32-96-0.25-5	$2.3E-{03}_{1.2E-2}$	$1.9E-{02}_{6.5E-2}$	$0.0E{00}_{0.0E0}$	$1.8E-{02}_{7.1E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$3.2E-{03}_{1.7E-2}$	$0.0E{00}_{0.0E0}$
Sparse-32-96-0.25-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$8.0E-{04}_{4.3E-3}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Sparse-32-96-0.5-0.1	$1.7E-{02}_{5.4E-2}$	$2.7E-{03}_{1.1E-2}$	$3.4E-{05}_{1.8E-4}$	$1.3E-{01}_{1.9E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.0E-{02}_{4.1E-2}$	$3.2E-{03}_{1.7E-2}$
Sparse-32-96-0.5-0.5	$1.3E-{01}_{1.2E-1}$	$1.9E-{01}_{1.5E-1}$	$2.3E-{02}_{5.1E-2}$	$2.9E-{01}_{1.9E-1}$	$2.1E-{02}_{3.9E-2}$	$3.8E-{02}_{6.8E-2}$	$1.4E-{01}_{1.7E-1}$	$3.9E-{02}_{5.3E-2}$
Sparse-32-96-0.5-1	$0.0E{00}_{0.0E0}$	$1.8E-{05}_{9.7E-5}$	$0.0E{00}_{0.0E0}$	$1.9E-{02}_{7.3E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Sparse-32-96-0.5-5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$9.4E-{03}_{2.8E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.2E-{04}_{6.6E-4}$	$0.0E{00}_{0.0E0}$
Sparse-32-96-0.5-10	$0.0E{00}_{0.0E0}$	$2.2E-{04}_{1.2E-3}$	$0.0E{00}_{0.0E0}$	$1.9E-{02}_{8.7E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Sparse-32-96-0.75-0.1	$0.0E{00}_{0.0E0}$	$1.1E-{03}_{5.8E-3}$	$0.0E{00}_{0.0E0}$	$5.5E-{02}_{1.6E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$9.0E-{04}_{4.8E-3}$	$0.0E{00}_{0.0E0}$
Sparse-32-96-0.75-0.5	$3.5E-{02}_{6.8E-2}$	$3.0E-{02}_{4.5E-2}$	$2.1E-{03}_{6.5E-3}$	$1.2E-{01}_{1.4E-1}$	$3.7E-{03}_{1.3E-2}$	$7.6E-{03}_{2.2E-2}$	$8.3E-{02}_{1.3E-1}$	$1.3E-{02}_{3.2E-2}$
Sparse-32-96-0.75-1	$0.0E{00}_{0.0E0}$	$5.4E-{05}_{2.9E-4}$	$0.0E{00}_{0.0E0}$	$8.4E-{03}_{4.2E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$7.9E-{03}_{4.3E-2}$	$0.0E{00}_{0.0E0}$
Sparse-32-96-0.75-5	$1.8E-{03}_{9.8E-3}$	$9.9E-{06}_{5.3E-5}$	$0.0E{00}_{0.0E0}$	$4.6E-{02}_{1.2E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Sparse-32-96-0.75-10	$0.0E{00}_{0.0E0}$	$1.6E-{03}_{8.8E-3}$	$0.0E{00}_{0.0E0}$	$1.3E-{02}_{3.6E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Sparse-32-96-1-0.1	$0.0E{00}_{0.0E0}$	$5.0E-{04}_{2.7E-3}$	$0.0E{00}_{0.0E0}$	$1.2E-{02}_{4.5E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$3.7E-{03}_{1.7E-2}$	$0.0E{00}_{0.0E0}$
Sparse-32-96-1-0.5	$5.7E-{02}_{1.4E-1}$	$2.3E-{02}_{7.0E-2}$	$3.0E-{04}_{1.6E-3}$	$8.8E-{02}_{1.8E-1}$	$3.5E-{03}_{1.5E-2}$	$6.1E-{03}_{3.0E-2}$	$1.4E-{02}_{3.9E-2}$	$9.1E-{04}_{4.9E-3}$
Sparse-32-96-1-1	$7.9E-{03}_{3.4E-2}$	$8.3E-{03}_{2.9E-2}$	$0.0E{00}_{0.0E0}$	$2.3E-{02}_{5.2E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$4.0E-{03}_{2.0E-2}$	$0.0E{00}_{0.0E0}$
Sparse-32-96-1-5	$1.2E-{02}_{6.3E-2}$	$1.0E-{02}_{5.4E-2}$	$0.0E{00}_{0.0E0}$	$4.6E-{02}_{1.2E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Sparse-32-96-1-10	$2.0E-{02}_{6.6E-2}$	$5.1E-{02}_{1.2E-1}$	$1.1E-{02}_{4.0E-2}$	$5.0E-{02}_{1.2E-1}$	$4.4E-{03}_{2.1E-2}$	$2.1E-{03}_{1.1E-2}$	$4.3E-{02}_{1.5E-1}$	$5.0E-{03}_{2.7E-2}$
Sparse-64-96-0.1-0.1	$9.7E-{04}_{5.2E-3}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$6.4E-{02}_{1.4E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Sparse-64-96-0.1-0.5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.4E-{02}_{5.1E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Sparse-64-96-0.1-1	$2.4E-{02}_{8.2E-2}$	$7.4E-{03}_{2.2E-2}$	$0.0E{00}_{0.0E0}$	$1.1E-{01}_{1.7E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.5E-{02}_{4.2E-2}$	$0.0E{00}_{0.0E0}$
Sparse-64-96-0.1-5	$3.1E-{02}_{8.4E-2}$	$6.4E-{02}_{1.3E-1}$	$3.2E-{03}_{1.5E-2}$	$2.1E-{02}_{5.8E-2}$	$1.8E-{02}_{4.5E-2}$	$1.3E-{02}_{6.9E-2}$	$2.4E-{02}_{7.3E-2}$	$1.7E-{02}_{4.5E-2}$
Sparse-64-96-0.1-10	$1.9E-{02}_{7.0E-2}$	$1.2E-{02}_{4.4E-2}$	$0.0E{00}_{0.0E0}$	$3.2E-{02}_{8.8E-2}$	$1.1E-{04}_{5.7E-4}$	$0.0E{00}_{0.0E0}$	$5.4E-{03}_{2.9E-2}$	$6.0E-{04}_{3.2E-3}$
Sparse-64-96-0.25-0.1	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$4.4E-{02}_{1.1E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Sparse-64-96-0.25-0.5	$1.5E-{03}_{8.1E-3}$	$7.4E-{03}_{3.6E-2}$	$0.0E{00}_{0.0E0}$	$7.8E-{02}_{1.9E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.1E-{02}_{3.9E-2}$	$0.0E{00}_{0.0E0}$
Sparse-64-96-0.25-1	$7.0E-{03}_{2.7E-2}$	$2.2E-{04}_{1.2E-3}$	$0.0E{00}_{0.0E0}$	$1.6E-{01}_{1.6E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$2.5E-{03}_{8.0E-3}$	$0.0E{00}_{0.0E0}$
Sparse-64-96-0.25-5	$0.0E{00}_{0.0E0}$	$1.4E-{02}_{7.8E-2}$	$0.0E{00}_{0.0E0}$	$1.2E-{02}_{4.1E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Sparse-64-96-0.25-10	$8.8E-{02}_{1.7E-1}$	$6.7E-{02}_{1.2E-1}$	$1.3E-{02}_{5.1E-2}$	$2.3E-{01}_{2.4E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$3.4E-{02}_{1.0E-1}$	$6.4E-{03}_{2.9E-2}$
Sparse-64-96-0.5-0.1	$1.9E-{03}_{7.5E-3}$	$1.1E-{03}_{3.5E-3}$	$0.0E{00}_{0.0E0}$	$9.5E-{02}_{1.3E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$9.9E-{04}_{5.4E-3}$	$0.0E{00}_{0.0E0}$
Sparse-64-96-0.5-0.5	$2.0E-{02}_{6.2E-2}$	$1.3E-{03}_{7.0E-3}$	$0.0E{00}_{0.0E0}$	$1.3E-{01}_{1.8E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$5.0E-{03}_{1.6E-2}$	$5.1E-{06}_{2.7E-5}$
Sparse-64-96-0.5-1	$6.8E-{02}_{1.1E-1}$	$6.7E-{02}_{9.6E-2}$	$3.0E-{03}_{7.4E-3}$	$2.1E-{01}_{2.0E-1}$	$2.1E-{03}_{1.1E-2}$	$6.4E-{04}_{3.3E-3}$	$8.2E-{02}_{1.3E-1}$	$2.1E-{02}_{4.8E-2}$
Sparse-64-96-0.5-5	$1.5E-{03}_{7.3E-3}$	$1.7E-{02}_{7.4E-2}$	$0.0E{00}_{0.0E0}$	$2.6E-{02}_{8.4E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.0E-{02}_{5.6E-2}$	$0.0E{00}_{0.0E0}$
Sparse-64-96-0.5-10	$0.0E{00}_{0.0E0}$	$1.3E-{02}_{6.8E-2}$	$0.0E{00}_{0.0E0}$	$5.7E-{03}_{1.7E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.4E-{02}_{5.7E-2}$	$0.0E{00}_{0.0E0}$
Sparse-64-96-0.75-0.1	$1.8E-{03}_{9.5E-3}$	$5.1E-{05}_{1.9E-4}$	$0.0E{00}_{0.0E0}$	$1.5E-{01}_{1.5E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$3.7E-{03}_{1.7E-2}$	$0.0E{00}_{0.0E0}$
Sparse-64-96-0.75-0.5	$0.0E{00}_{0.0E0}$	$6.6E-{04}_{3.5E-3}$	$0.0E{00}_{0.0E0}$	$3.2E-{02}_{9.7E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Sparse-64-96-0.75-1	$7.4E-{02}_{1.1E-1}$	$9.4E-{02}_{9.6E-2}$	$4.9E-{03}_{1.9E-2}$	$2.2E-{01}_{1.6E-1}$	$6.5E-{04}_{3.5E-3}$	$5.2E-{03}_{1.7E-2}$	$8.7E-{02}_{7.9E-2}$	$6.8E-{03}_{2.3E-2}$
Sparse-64-96-0.75-5	$3.2E-{03}_{1.7E-2}$	$1.9E-{03}_{9.4E-3}$	$0.0E{00}_{0.0E0}$	$9.5E-{03}_{5.0E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$4.2E-{04}_{2.3E-3}$	$0.0E{00}_{0.0E0}$
Sparse-64-96-0.75-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$1.3E-{02}_{6.9E-2}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Sparse-64-96-1-0.1	$1.4E-{02}_{3.4E-2}$	$1.7E-{02}_{5.5E-2}$	$0.0E{00}_{0.0E0}$	$1.8E-{01}_{1.9E-1}$	$1.4E-{03}_{7.3E-3}$	$8.0E-{06}_{4.3E-5}$	$1.1E-{02}_{2.4E-2}$	$0.0E{00}_{0.0E0}$
Sparse-64-96-1-0.5	$3.2E-{02}_{7.0E-2}$	$2.9E-{02}_{7.2E-2}$	$0.0E{00}_{0.0E0}$	$7.8E-{02}_{1.1E-1}$	$9.4E-{04}_{4.9E-3}$	$3.2E-{03}_{1.5E-2}$	$9.3E-{03}_{3.2E-2}$	$6.6E-{03}_{1.9E-2}$
Sparse-64-96-1-1	$1.9E-{02}_{8.2E-2}$	$1.0E-{02}_{4.4E-2}$	$0.0E{00}_{0.0E0}$	$4.3E-{02}_{1.3E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$2.2E-{02}_{9.7E-2}$	$7.5E-{04}_{3.5E-3}$
Sparse-64-96-1-5	$0.0E{00}_{0.0E0}$	$1.5E-{03}_{8.3E-3}$	$0.0E{00}_{0.0E0}$	$4.3E-{02}_{1.3E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$2.8E-{02}_{1.2E-1}$	$0.0E{00}_{0.0E0}$
Sparse-64-96-1-10	$5.5E-{03}_{1.8E-2}$	$7.7E-{03}_{3.4E-2}$	$0.0E{00}_{0.0E0}$	$1.0E-{01}_{1.7E-1}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$2.3E-{03}_{1.2E-2}$	$0.0E{00}_{0.0E0}$

,

Table 7. IGD+ mean and standard deviation of the eight algorithms over 30 independent runs. Dark/light gray emphasize the best/second-best results.

Problem	AGEMOEA	AGEMOEA2	GWASFGA	MOCell	MOMBI	MOMBI2	NSGA2	SMS-EMOA
Fpppp-8-334-0.1-0.1	$2.6E-{01}_{6.7E-2}$	$2.5E-{01}_{4.5E-2}$	$3.8E-{01}_{6.6E-2}$	$1.5E-{01}_{7.1E-2}$	$3.5E-{01}_{7.6E-2}$	$3.5E-{01}_{6.1E-2}$	$2.4E-{01}_{5.6E-2}$	$3.6E-{01}_{7.2E-2}$
Fpppp-8-334-0.1-0.5	$3.2E-{01}_{8.8E-2}$	$3.0E-{01}_{8.0E-2}$	$5.3E-{01}_{9.8E-2}$	$2.4E-{01}_{7.5E-2}$	$4.7E-{01}_{8.0E-2}$	$5.2E-{01}_{1.1E-1}$	$3.3E-{01}_{7.7E-2}$	$4.6E-{01}_{9.4E-2}$
Fpppp-8-334-0.1-1	$4.2E-{01}_{9.4E-2}$	$4.8E-{01}_{9.9E-2}$	$6.5E-{01}_{1.3E-1}$	$3.0E-{01}_{9.7E-2}$	$6.7E-{01}_{1.2E-1}$	$6.3E-{01}_{1.1E-1}$	$4.4E-{01}_{1.2E-1}$	$6.2E-{01}_{1.3E-1}$
Fpppp-8-334-0.1-5	$1.7E{00}_{4.5E-1}$	$1.6E{00}_{4.9E-1}$	$2.1E{00}_{3.6E-1}$	$1.5E{00}_{6.1E-1}$	$2.0E{00}_{3.9E-1}$	$2.1E{00}_{4.5E-1}$	$1.6E{00}_{5.9E-1}$	$1.9E{00}_{5.0E-1}$
Fpppp-8-334-0.1-10	$4.8E{00}_{2.1E0}$	$5.7E{00}_{3.1E0}$	$7.8E{00}_{2.8E0}$	$4.9E{00}_{2.6E0}$	$6.9E{00}_{2.6E0}$	$6.6E{00}_{1.9E0}$	$4.7E{00}_{2.6E0}$	$6.0E{00}_{2.3E0}$
Fpppp-8-334-0.25-0.1	$3.1E-{01}_{8.6E-2}$	$3.3E-{01}_{1.0E-1}$	$5.2E-{01}_{8.0E-2}$	$2.2E-{01}_{9.0E-2}$	$4.4E-{01}_{9.1E-2}$	$4.9E-{01}_{7.2E-2}$	$3.3E-{01}_{8.3E-2}$	$4.5E-{01}_{6.8E-2}$
Fpppp-8-334-0.25-0.5	$1.4E-{01}_{5.0E-2}$	$1.4E-{01}_{3.9E-2}$	$2.3E-{01}_{4.0E-2}$	$1.0E-{01}_{4.3E-2}$	$2.1E-{01}_{3.6E-2}$	$2.1E-{01}_{4.0E-2}$	$1.5E-{01}_{2.6E-2}$	$2.0E-{01}_{3.7E-2}$
Fpppp-8-334-0.25-1	$9.1E-{01}_{2.7E-1}$	$9.2E-{01}_{3.9E-1}$	$1.3E{00}_{3.8E-1}$	$7.6E-{01}_{3.0E-1}$	$1.3E{00}_{3.3E-1}$	$1.6E{00}_{3.4E-1}$	$8.7E-{01}_{2.9E-1}$	$1.4E{00}_{3.4E-1}$
Fpppp-8-334-0.25-5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-8-334-0.25-10	$1.4E{00}_{6.0E-1}$	$1.3E{00}_{7.2E-1}$	$1.5E{00}_{6.5E-1}$	$1.8E{00}_{9.6E-1}$	$1.6E{00}_{8.4E-1}$	$1.4E{00}_{6.3E-1}$	$1.2E{00}_{7.2E-1}$	$1.6E{00}_{6.9E-1}$
Fpppp-8-334-0.5-0.1	$1.9E-{01}_{4.7E-2}$	$1.9E-{01}_{5.2E-2}$	$2.8E-{01}_{7.0E-2}$	$1.6E-{01}_{4.8E-2}$	$2.7E-{01}_{5.9E-2}$	$2.6E-{01}_{4.4E-2}$	$1.8E-{01}_{5.0E-2}$	$2.6E-{01}_{4.7E-2}$
Fpppp-8-334-0.5-0.5	$2.8E-{01}_{1.1E-1}$	$2.9E-{01}_{7.9E-2}$	$4.5E-{01}_{9.7E-2}$	$2.0E-{01}_{9.7E-2}$	$4.2E-{01}_{8.6E-2}$	$3.9E-{01}_{9.7E-2}$	$2.8E-{01}_{1.0E-1}$	$4.2E-{01}_{1.1E-1}$
Fpppp-8-334-0.5-1	$3.1E-{01}_{9.5E-2}$	$3.4E-{01}_{8.4E-2}$	$5.1E-{01}_{8.1E-2}$	$2.2E-{01}_{8.1E-2}$	$4.6E-{01}_{7.5E-2}$	$4.3E-{01}_{9.4E-2}$	$3.2E-{01}_{1.1E-1}$	$4.5E-{01}_{1.1E-1}$
Fpppp-8-334-0.5-5	$1.8E{00}_{8.4E-1}$	$1.8E{00}_{9.4E-1}$	$2.2E{00}_{6.1E-1}$	$2.0E{00}_{1.4E0}$	$2.5E{00}_{7.9E-1}$	$2.5E{00}_{5.3E-1}$	$1.7E{00}_{7.7E-1}$	$2.0E{00}_{8.6E-1}$
Fpppp-8-334-0.5-10	$4.8E{00}_{2.2E0}$	$5.1E{00}_{2.3E0}$	$6.2E{00}_{2.0E0}$	$5.7E{00}_{2.4E0}$	$6.1E{00}_{2.0E0}$	$6.4E{00}_{1.7E0}$	$5.0E{00}_{2.5E0}$	$5.5E{00}_{2.1E0}$
Fpppp-8-334-0.75-0.1	$3.0E-{01}_{1.2E-1}$	$2.8E-{01}_{1.1E-1}$	$5.2E-{01}_{1.3E-1}$	$2.6E-{01}_{1.4E-1}$	$3.9E-{01}_{9.4E-2}$	$5.2E-{01}_{1.2E-1}$	$3.2E-{01}_{1.3E-1}$	$4.2E-{01}_{1.2E-1}$
Fpppp-8-334-0.75-0.5	$1.6E-{01}_{5.5E-2}$	$1.7E-{01}_{3.7E-2}$	$2.4E-{01}_{7.2E-2}$	$1.6E-{01}_{5.6E-2}$	$2.2E-{01}_{6.1E-2}$	$2.2E-{01}_{6.0E-2}$	$1.6E-{01}_{5.4E-2}$	$2.4E-{01}_{5.2E-2}$
Fpppp-8-334-0.75-1	$4.2E-{01}_{1.3E-1}$	$4.1E-{01}_{1.8E-1}$	$6.6E-{01}_{1.4E-1}$	$4.3E-{01}_{1.8E-1}$	$6.5E-{01}_{1.6E-1}$	$5.5E-{01}_{1.6E-1}$	$4.3E-{01}_{1.6E-1}$	$5.9E-{01}_{1.6E-1}$
Fpppp-8-334-0.75-5	$8.7E{00}_{3.0E0}$	$8.4E{00}_{3.7E0}$	$1.2E{01}_{4.1E0}$	$7.8E{00}_{3.6E0}$	$1.1E{01}_{3.1E0}$	$1.1E{01}_{3.3E0}$	$9.8E{00}_{3.4E0}$	$1.1E{01}_{2.8E0}$
Fpppp-8-334-0.75-10	$1.9E{00}_{9.3E-1}$	$2.1E{00}_{1.2E0}$	$2.9E{00}_{1.2E0}$	$3.0E{00}_{1.5E0}$	$2.8E{00}_{1.0E0}$	$2.4E{00}_{6.6E-1}$	$2.1E{00}_{9.2E-1}$	$2.0E{00}_{7.2E-1}$
Fpppp-8-334-1-0.1	$1.6E{00}_{5.8E-1}$	$1.3E{00}_{5.1E-1}$	$2.5E{00}_{6.8E-1}$	$1.8E{00}_{8.4E-1}$	$2.2E{00}_{5.7E-1}$	$2.4E{00}_{6.2E-1}$	$1.3E{00}_{4.8E-1}$	$2.1E{00}_{6.5E-1}$
Fpppp-8-334-1-0.5	$3.6E-{01}_{1.2E-1}$	$2.9E-{01}_{1.2E-1}$	$5.6E-{01}_{1.3E-1}$	$3.5E-{01}_{1.3E-1}$	$4.7E-{01}_{9.3E-2}$	$4.7E-{01}_{8.8E-2}$	$3.0E-{01}_{1.2E-1}$	$4.1E-{01}_{9.2E-2}$
Fpppp-8-334-1-1	$5.8E-{01}_{1.9E-1}$	$5.2E-{01}_{1.7E-1}$	$8.2E-{01}_{1.8E-1}$	$5.3E-{01}_{2.3E-1}$	$7.9E-{01}_{1.6E-1}$	$7.3E-{01}_{1.6E-1}$	$5.5E-{01}_{1.9E-1}$	$7.3E-{01}_{1.5E-1}$
Fpppp-8-334-1-5	$2.6E{00}_{1.3E0}$	$2.9E{00}_{1.2E0}$	$3.3E{00}_{1.1E0}$	$3.6E{00}_{1.4E0}$	$3.8E{00}_{1.3E0}$	$4.0E{00}_{1.5E0}$	$3.1E{00}_{9.4E-1}$	$3.4E{00}_{9.5E-1}$
Fpppp-8-334-1-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-16-334-0.1-0.1	$2.8E-{01}_{9.0E-2}$	$2.6E-{01}_{9.3E-2}$	$4.9E-{01}_{8.2E-2}$	$1.8E-{01}_{8.0E-2}$	$4.7E-{01}_{8.3E-2}$	$5.0E-{01}_{9.4E-2}$	$2.7E-{01}_{8.8E-2}$	$4.4E-{01}_{1.0E-1}$
Fpppp-16-334-0.1-0.5	$1.7E{01}_{6.0E0}$	$2.0E{01}_{4.6E0}$	$2.5E{01}_{5.9E0}$	$1.3E{01}_{5.0E0}$	$2.7E{01}_{5.3E0}$	$2.6E{01}_{5.7E0}$	$2.0E{01}_{4.9E0}$	$2.9E{01}_{5.6E0}$
Fpppp-16-334-0.1-1	$3.8E{00}_{1.4E0}$	$3.7E{00}_{1.3E0}$	$6.4E{00}_{9.1E-1}$	$2.9E{00}_{1.1E0}$	$6.0E{00}_{9.8E-1}$	$6.3E{00}_{1.2E0}$	$3.3E{00}_{1.2E0}$	$5.4E{00}_{1.2E0}$
Fpppp-16-334-0.1-5	$4.6E{00}_{1.3E0}$	$4.5E{00}_{1.2E0}$	$6.0E{00}_{1.3E0}$	$4.4E{00}_{1.4E0}$	$5.6E{00}_{1.4E0}$	$5.5E{00}_{1.3E0}$	$4.0E{00}_{1.3E0}$	$5.8E{00}_{1.2E0}$
Fpppp-16-334-0.1-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-16-334-0.25-0.1	$4.4E-{01}_{1.6E-1}$	$5.7E-{01}_{1.2E-1}$	$8.1E-{01}_{1.8E-1}$	$3.1E-{01}_{1.3E-1}$	$7.8E-{01}_{1.4E-1}$	$7.4E-{01}_{1.5E-1}$	$4.8E-{01}_{1.3E-1}$	$7.1E-{01}_{1.8E-1}$
Fpppp-16-334-0.25-0.5	$4.8E-{01}_{1.6E-1}$	$5.1E-{01}_{1.6E-1}$	$8.7E-{01}_{1.8E-1}$	$3.5E-{01}_{1.6E-1}$	$8.5E-{01}_{2.0E-1}$	$8.8E-{01}_{1.8E-1}$	$5.5E-{01}_{1.2E-1}$	$7.2E-{01}_{1.5E-1}$
Fpppp-16-334-0.25-1	$1.4E{00}_{6.3E-1}$	$1.5E{00}_{5.5E-1}$	$2.4E{00}_{5.6E-1}$	$9.5E-{01}_{4.0E-1}$	$2.2E{00}_{4.8E-1}$	$2.4E{00}_{5.6E-1}$	$1.4E{00}_{5.1E-1}$	$2.2E{00}_{7.2E-1}$
Fpppp-16-334-0.25-5	$7.0E{00}_{3.0E0}$	$6.9E{00}_{2.8E0}$	$9.2E{00}_{3.3E0}$	$8.2E{00}_{2.9E0}$	$8.8E{00}_{3.5E0}$	$7.7E{00}_{2.5E0}$	$7.1E{00}_{3.1E0}$	$7.0E{00}_{2.4E0}$
Fpppp-16-334-0.25-10	$2.8E{01}_{2.2E1}$	$3.2E{01}_{2.0E1}$	$3.0E{01}_{1.2E1}$	$4.0E{01}_{2.5E1}$	$3.7E{01}_{2.0E1}$	$3.0E{01}_{1.1E1}$	$2.7E{01}_{1.6E1}$	$3.9E{01}_{2.4E1}$
Fpppp-16-334-0.5-0.1	$4.4E-{01}_{1.3E-1}$	$4.6E-{01}_{1.4E-1}$	$8.1E-{01}_{1.9E-1}$	$3.5E-{01}_{1.6E-1}$	$7.8E-{01}_{1.6E-1}$	$8.5E-{01}_{1.7E-1}$	$4.8E-{01}_{1.6E-1}$	$8.0E-{01}_{1.5E-1}$
Fpppp-16-334-0.5-0.5	$5.1E-{01}_{1.6E-1}$	$4.8E-{01}_{1.5E-1}$	$8.1E-{01}_{1.3E-1}$	$3.5E-{01}_{1.4E-1}$	$8.5E-{01}_{1.7E-1}$	$7.9E-{01}_{1.5E-1}$	$5.1E-{01}_{1.6E-1}$	$7.9E-{01}_{1.4E-1}$
Fpppp-16-334-0.5-1	$7.2E-{01}_{1.9E-1}$	$6.7E-{01}_{2.2E-1}$	$1.1E{00}_{2.1E-1}$	$5.3E-{01}_{1.9E-1}$	$1.0E{00}_{2.4E-1}$	$1.1E{00}_{2.3E-1}$	$7.3E-{01}_{2.1E-1}$	$1.0E{00}_{2.4E-1}$
Fpppp-16-334-0.5-5	$1.8E{00}_{1.0E0}$	$1.5E{00}_{7.2E-1}$	$2.3E{00}_{9.3E-1}$	$1.8E{00}_{8.6E-1}$	$2.1E{00}_{7.8E-1}$	$2.2E{00}_{7.9E-1}$	$1.5E{00}_{7.3E-1}$	$2.2E{00}_{1.1E0}$
Fpppp-16-334-0.5-10	$4.1E{00}_{2.1E0}$	$4.0E{00}_{2.2E0}$	$6.0E{00}_{2.3E0}$	$7.0E{00}_{2.6E0}$	$6.3E{00}_{2.1E0}$	$5.2E{00}_{1.7E0}$	$4.1E{00}_{2.1E0}$	$6.2E{00}_{2.9E0}$
Fpppp-16-334-0.75-0.1	$6.5E{00}_{3.4E0}$	$6.6E{00}_{3.6E0}$	$1.1E{01}_{3.8E0}$	$6.4E{00}_{3.3E0}$	$1.3E{01}_{3.2E0}$	$9.7E{00}_{3.8E0}$	$6.4E{00}_{3.9E0}$	$1.0E{01}_{3.7E0}$
Fpppp-16-334-0.75-0.5	$6.7E-{01}_{2.0E-1}$	$6.1E-{01}_{1.6E-1}$	$9.2E-{01}_{1.9E-1}$	$5.1E-{01}_{2.0E-1}$	$1.0E{00}_{2.4E-1}$	$9.2E-{01}_{1.9E-1}$	$7.7E-{01}_{2.1E-1}$	$9.5E-{01}_{1.8E-1}$
Fpppp-16-334-0.75-1	$4.9E{00}_{1.1E0}$	$4.8E{00}_{1.8E0}$	$7.6E{00}_{2.1E0}$	$4.2E{00}_{2.1E0}$	$8.2E{00}_{2.1E0}$	$7.4E{00}_{2.4E0}$	$4.9E{00}_{2.5E0}$	$7.8E{00}_{2.3E0}$
Fpppp-16-334-0.75-5	$3.9E{01}_{1.6E1}$	$3.8E{01}_{1.4E1}$	$4.7E{01}_{1.6E1}$	$3.9E{01}_{1.5E1}$	$5.3E{01}_{1.4E1}$	$5.3E{01}_{1.5E1}$	$3.8E{01}_{1.5E1}$	$5.4E{01}_{1.5E1}$
Fpppp-16-334-0.75-10	$8.2E{01}_{5.0E1}$	$8.2E{01}_{4.8E1}$	$1.2E{02}_{3.1E1}$	$1.2E{02}_{5.3E1}$	$1.1E{02}_{3.7E1}$	$1.2E{02}_{4.0E1}$	$8.8E{01}_{5.3E1}$	$9.9E{01}_{5.5E1}$
Fpppp-16-334-1-0.1	$9.3E-{01}_{3.9E-1}$	$9.6E-{01}_{3.8E-1}$	$1.8E{00}_{3.8E-1}$	$1.1E{00}_{3.6E-1}$	$1.6E{00}_{3.5E-1}$	$1.5E{00}_{4.0E-1}$	$9.2E-{01}_{4.6E-1}$	$1.5E{00}_{4.2E-1}$
Fpppp-16-334-1-0.5	$8.6E-{01}_{3.1E-1}$	$9.1E-{01}_{3.6E-1}$	$1.4E{00}_{3.4E-1}$	$9.0E-{01}_{4.1E-1}$	$1.4E{00}_{3.6E-1}$	$1.4E{00}_{3.3E-1}$	$8.2E-{01}_{3.9E-1}$	$1.2E{00}_{3.8E-1}$
Fpppp-16-334-1-1	$4.6E{00}_{1.2E0}$	$4.5E{00}_{2.3E0}$	$6.1E{00}_{1.6E0}$	$3.8E{00}_{1.8E0}$	$6.2E{00}_{1.5E0}$	$5.6E{00}_{1.4E0}$	$3.8E{00}_{1.5E0}$	$6.1E{00}_{1.4E0}$
Fpppp-16-334-1-5	$2.1E{01}_{6.7E0}$	$2.0E{01}_{6.3E0}$	$2.4E{01}_{6.7E0}$	$2.3E{01}_{8.2E0}$	$2.3E{01}_{8.8E0}$	$2.1E{01}_{8.3E0}$	$2.1E{01}_{8.3E0}$	$2.0E{01}_{7.6E0}$
Fpppp-16-334-1-10	$1.2E{01}_{4.3E0}$	$1.1E{01}_{3.9E0}$	$1.3E{01}_{4.2E0}$	$1.3E{01}_{5.2E0}$	$1.3E{01}_{3.8E0}$	$1.3E{01}_{4.0E0}$	$1.0E{01}_{4.8E0}$	$1.1E{01}_{3.8E0}$
Fpppp-32-334-0.1-0.1	$6.5E-{01}_{3.0E-1}$	$6.8E-{01}_{2.2E-1}$	$1.3E{00}_{3.0E-1}$	$4.1E-{01}_{2.6E-1}$	$1.2E{00}_{3.5E-1}$	$1.3E{00}_{2.7E-1}$	$7.1E-{01}_{2.5E-1}$	$1.2E{00}_{3.4E-1}$
Fpppp-32-334-0.1-0.5	$1.1E{00}_{3.5E-1}$	$1.2E{00}_{4.4E-1}$	$2.0E{00}_{4.5E-1}$	$6.9E-{01}_{3.5E-1}$	$2.0E{00}_{4.1E-1}$	$2.0E{00}_{4.9E-1}$	$1.2E{00}_{4.0E-1}$	$2.0E{00}_{5.0E-1}$
Fpppp-32-334-0.1-1	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-0.1-5	$1.7E{00}_{7.1E-1}$	$1.7E{00}_{6.4E-1}$	$2.3E{00}_{7.3E-1}$	$1.5E{00}_{7.5E-1}$	$2.3E{00}_{6.6E-1}$	$2.3E{00}_{6.7E-1}$	$1.8E{00}_{8.5E-1}$	$2.1E{00}_{7.0E-1}$
Fpppp-32-334-0.1-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-0.25-0.1	$3.1E{00}_{8.6E-1}$	$3.7E{00}_{1.1E0}$	$6.3E{00}_{1.0E0}$	$3.0E{00}_{1.1E0}$	$5.8E{00}_{9.4E-1}$	$5.8E{00}_{1.1E0}$	$3.5E{00}_{8.6E-1}$	$5.8E{00}_{1.2E0}$
Fpppp-32-334-0.25-0.5	$4.4E{00}_{2.1E0}$	$5.0E{00}_{1.7E0}$	$9.7E{00}_{2.3E0}$	$3.8E{00}_{1.8E0}$	$8.1E{00}_{1.9E0}$	$1.0E{01}_{1.9E0}$	$5.1E{00}_{1.7E0}$	$9.1E{00}_{2.0E0}$
Fpppp-32-334-0.25-1	$4.5E{01}_{1.2E1}$	$4.7E{01}_{1.1E1}$	$6.8E{01}_{1.0E1}$	$3.9E{01}_{1.6E1}$	$6.2E{01}_{9.7E0}$	$6.6E{01}_{1.3E1}$	$4.6E{01}_{1.3E1}$	$6.2E{01}_{1.2E1}$
Fpppp-32-334-0.25-5	$1.5E{01}_{4.2E0}$	$1.5E{01}_{4.6E0}$	$2.0E{01}_{4.7E0}$	$1.6E{01}_{4.7E0}$	$1.9E{01}_{4.4E0}$	$2.0E{01}_{4.9E0}$	$1.7E{01}_{3.6E0}$	$1.7E{01}_{5.4E0}$
Fpppp-32-334-0.25-10	$1.7E{01}_{7.4E0}$	$1.5E{01}_{7.9E0}$	$2.3E{01}_{6.5E0}$	$1.9E{01}_{5.9E0}$	$2.5E{01}_{8.1E0}$	$2.1E{01}_{8.4E0}$	$1.8E{01}_{7.4E0}$	$2.1E{01}_{7.6E0}$
Fpppp-32-334-0.5-0.1	$7.5E-{01}_{2.3E-1}$	$6.3E-{01}_{2.1E-1}$	$1.1E{00}_{2.7E-1}$	$5.1E-{01}_{2.6E-1}$	$1.2E{00}_{2.5E-1}$	$1.2E{00}_{2.4E-1}$	$6.1E-{01}_{2.2E-1}$	$1.1E{00}_{2.1E-1}$
Fpppp-32-334-0.5-0.5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-0.5-1	$3.4E{01}_{1.1E1}$	$3.2E{01}_{1.4E1}$	$5.3E{01}_{1.5E1}$	$3.3E{01}_{1.3E1}$	$5.1E{01}_{1.2E1}$	$5.7E{01}_{1.1E1}$	$3.3E{01}_{1.3E1}$	$5.2E{01}_{9.7E0}$
Fpppp-32-334-0.5-5	$2.6E{01}_{1.0E1}$	$2.6E{01}_{1.0E1}$	$3.0E{01}_{1.1E1}$	$2.7E{01}_{1.1E1}$	$3.8E{01}_{8.9E0}$	$3.2E{01}_{1.1E1}$	$2.7E{01}_{1.3E1}$	$3.0E{01}_{1.1E1}$
Fpppp-32-334-0.5-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-0.75-0.1	$2.7E{00}_{1.2E0}$	$2.7E{00}_{1.4E0}$	$4.8E{00}_{1.1E0}$	$2.3E{00}_{1.1E0}$	$5.5E{00}_{9.8E-1}$	$4.6E{00}_{1.4E0}$	$2.7E{00}_{1.3E0}$	$5.0E{00}_{1.3E0}$
Fpppp-32-334-0.75-0.5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-0.75-1	$1.2E{00}_{4.3E-1}$	$9.0E-{01}_{4.2E-1}$	$1.6E{00}_{5.5E-1}$	$8.2E-{01}_{4.1E-1}$	$1.5E{00}_{4.4E-1}$	$1.6E{00}_{3.2E-1}$	$8.9E-{01}_{4.6E-1}$	$1.6E{00}_{4.7E-1}$
Fpppp-32-334-0.75-5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-0.75-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-1-0.1	$1.2E{00}_{4.5E-1}$	$1.3E{00}_{5.6E-1}$	$2.1E{00}_{5.1E-1}$	$1.4E{00}_{5.4E-1}$	$2.1E{00}_{6.2E-1}$	$2.5E{00}_{6.7E-1}$	$1.3E{00}_{5.0E-1}$	$2.1E{00}_{4.3E-1}$
Fpppp-32-334-1-0.5	$3.4E{00}_{1.1E0}$	$3.8E{00}_{1.1E0}$	$5.5E{00}_{9.0E-1}$	$3.2E{00}_{1.4E0}$	$5.3E{00}_{1.1E0}$	$5.1E{00}_{1.2E0}$	$3.6E{00}_{1.1E0}$	$4.8E{00}_{1.2E0}$
Fpppp-32-334-1-1	$1.5E{00}_{5.8E-1}$	$1.5E{00}_{6.8E-1}$	$2.4E{00}_{6.1E-1}$	$1.5E{00}_{6.2E-1}$	$2.4E{00}_{6.7E-1}$	$2.4E{00}_{6.5E-1}$	$1.1E{00}_{4.9E-1}$	$2.3E{00}_{8.1E-1}$
Fpppp-32-334-1-5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-1-10	$2.3E{01}_{9.7E0}$	$2.2E{01}_{1.0E1}$	$3.2E{01}_{8.2E0}$	$2.4E{01}_{6.8E0}$	$3.0E{01}_{7.2E0}$	$3.0E{01}_{9.5E0}$	$2.5E{01}_{7.5E0}$	$2.8E{01}_{7.7E0}$
Fpppp-64-334-0.1-0.1	$9.7E-{01}_{3.1E-1}$	$8.9E-{01}_{2.8E-1}$	$1.6E{00}_{3.1E-1}$	$7.0E-{01}_{2.4E-1}$	$1.7E{00}_{2.9E-1}$	$1.5E{00}_{4.3E-1}$	$9.6E-{01}_{2.5E-1}$	$1.5E{00}_{3.0E-1}$
Fpppp-64-334-0.1-0.5	$7.7E-{01}_{2.2E-1}$	$7.1E-{01}_{2.7E-1}$	$1.4E{00}_{2.1E-1}$	$3.7E-{01}_{2.1E-1}$	$1.4E{00}_{2.3E-1}$	$1.3E{00}_{2.6E-1}$	$8.4E-{01}_{1.9E-1}$	$1.3E{00}_{3.0E-1}$
Fpppp-64-334-0.1-1	$4.8E-{01}_{2.1E-1}$	$4.2E-{01}_{1.4E-1}$	$7.7E-{01}_{2.0E-1}$	$2.6E-{01}_{1.2E-1}$	$7.9E-{01}_{1.4E-1}$	$7.3E-{01}_{1.8E-1}$	$5.1E-{01}_{1.8E-1}$	$6.8E-{01}_{2.1E-1}$
Fpppp-64-334-0.1-5	$9.2E-{01}_{4.7E-1}$	$9.1E-{01}_{4.2E-1}$	$1.2E{00}_{3.6E-1}$	$6.4E-{01}_{3.3E-1}$	$1.3E{00}_{4.2E-1}$	$1.5E{00}_{4.8E-1}$	$9.3E-{01}_{4.1E-1}$	$1.0E{00}_{3.6E-1}$
Fpppp-64-334-0.1-10	$2.5E{00}_{5.1E-1}$	$2.6E{00}_{4.1E-1}$	$3.1E{00}_{5.7E-1}$	$1.6E{00}_{6.0E-1}$	$3.3E{00}_{5.5E-1}$	$3.5E{00}_{8.6E-1}$	$2.5E{00}_{5.5E-1}$	$3.2E{00}_{6.0E-1}$
Fpppp-64-334-0.25-0.1	$9.3E-{01}_{2.3E-1}$	$9.2E-{01}_{3.6E-1}$	$1.8E{00}_{2.8E-1}$	$5.8E-{01}_{2.8E-1}$	$1.7E{00}_{2.7E-1}$	$1.7E{00}_{3.1E-1}$	$9.7E-{01}_{3.9E-1}$	$1.5E{00}_{2.9E-1}$
Fpppp-64-334-0.25-0.5	$5.2E-{01}_{1.9E-1}$	$4.9E-{01}_{1.9E-1}$	$9.3E-{01}_{2.2E-1}$	$2.8E-{01}_{1.4E-1}$	$9.2E-{01}_{1.7E-1}$	$9.7E-{01}_{1.7E-1}$	$5.7E-{01}_{1.0E-1}$	$9.1E-{01}_{2.0E-1}$
Fpppp-64-334-0.25-1	$5.5E-{01}_{2.6E-1}$	$5.9E-{01}_{1.7E-1}$	$8.5E-{01}_{2.2E-1}$	$3.6E-{01}_{1.9E-1}$	$8.2E-{01}_{2.0E-1}$	$9.2E-{01}_{1.7E-1}$	$5.7E-{01}_{2.3E-1}$	$8.2E-{01}_{2.4E-1}$
Fpppp-64-334-0.25-5	$6.3E-{01}_{2.3E-1}$	$7.6E-{01}_{3.3E-1}$	$1.0E{00}_{3.4E-1}$	$3.8E-{01}_{2.0E-1}$	$9.8E-{01}_{3.0E-1}$	$1.2E{00}_{3.1E-1}$	$6.1E-{01}_{2.3E-1}$	$7.7E-{01}_{2.2E-1}$
Fpppp-64-334-0.25-10	$3.0E{01}_{1.4E1}$	$3.3E{01}_{1.4E1}$	$3.6E{01}_{1.4E1}$	$2.8E{01}_{1.3E1}$	$3.3E{01}_{1.3E1}$	$4.6E{01}_{1.8E1}$	$2.9E{01}_{1.4E1}$	$3.0E{01}_{1.5E1}$
Fpppp-64-334-0.5-0.1	$3.7E{00}_{1.0E0}$	$3.9E{00}_{9.9E-1}$	$6.2E{00}_{1.0E0}$	$2.6E{00}_{1.1E0}$	$6.0E{00}_{1.1E0}$	$6.0E{00}_{1.2E0}$	$3.7E{00}_{1.0E0}$	$5.8E{00}_{1.2E0}$
Fpppp-64-334-0.5-0.5	$1.2E{00}_{4.1E-1}$	$1.2E{00}_{3.0E-1}$	$2.0E{00}_{3.5E-1}$	$8.5E-{01}_{3.4E-1}$	$2.0E{00}_{3.4E-1}$	$2.2E{00}_{4.8E-1}$	$1.3E{00}_{3.7E-1}$	$1.8E{00}_{4.2E-1}$
Fpppp-64-334-0.5-1	$1.6E{00}_{3.7E-1}$	$1.6E{00}_{4.7E-1}$	$2.2E{00}_{5.2E-1}$	$8.3E-{01}_{4.5E-1}$	$2.2E{00}_{4.8E-1}$	$2.1E{00}_{5.2E-1}$	$1.5E{00}_{4.8E-1}$	$2.2E{00}_{4.8E-1}$
Fpppp-64-334-0.5-5	$1.1E{00}_{4.3E-1}$	$1.2E{00}_{4.5E-1}$	$1.5E{00}_{4.3E-1}$	$8.7E-{01}_{4.8E-1}$	$1.4E{00}_{5.1E-1}$	$1.9E{00}_{4.8E-1}$	$1.2E{00}_{4.9E-1}$	$1.2E{00}_{4.7E-1}$
Fpppp-64-334-0.5-10	$1.8E{00}_{7.3E-1}$	$1.7E{00}_{7.0E-1}$	$2.4E{00}_{6.2E-1}$	$1.7E{00}_{6.0E-1}$	$2.4E{00}_{6.3E-1}$	$3.1E{00}_{7.7E-1}$	$2.0E{00}_{7.5E-1}$	$2.2E{00}_{5.5E-1}$
Fpppp-64-334-0.75-0.1	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-64-334-0.75-0.5	$5.6E-{01}_{2.1E-1}$	$6.1E-{01}_{2.3E-1}$	$1.0E{00}_{3.0E-1}$	$4.5E-{01}_{2.0E-1}$	$9.9E-{01}_{2.4E-1}$	$1.1E{00}_{2.8E-1}$	$6.2E-{01}_{2.6E-1}$	$9.3E-{01}_{2.9E-1}$
Fpppp-64-334-0.75-1	$7.9E-{01}_{3.2E-1}$	$9.4E-{01}_{3.3E-1}$	$1.3E{00}_{2.9E-1}$	$5.2E-{01}_{2.6E-1}$	$1.3E{00}_{3.4E-1}$	$1.1E{00}_{3.2E-1}$	$8.7E-{01}_{3.2E-1}$	$1.2E{00}_{3.6E-1}$
Fpppp-64-334-0.75-5	$1.5E{00}_{4.8E-1}$	$1.5E{00}_{4.8E-1}$	$1.9E{00}_{4.6E-1}$	$9.3E-{01}_{3.9E-1}$	$1.8E{00}_{4.8E-1}$	$2.0E{00}_{5.3E-1}$	$1.5E{00}_{5.5E-1}$	$1.9E{00}_{6.1E-1}$
Fpppp-64-334-0.75-10	$2.9E{00}_{6.6E-1}$	$2.6E{00}_{7.2E-1}$	$3.6E{00}_{7.3E-1}$	$1.8E{00}_{6.0E-1}$	$3.5E{00}_{8.9E-1}$	$4.3E{00}_{9.0E-1}$	$2.7E{00}_{8.9E-1}$	$3.2E{00}_{7.1E-1}$
Fpppp-64-334-1-0.1	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-64-334-1-0.5	$1.0E{00}_{3.5E-1}$	$1.1E{00}_{3.8E-1}$	$1.9E{00}_{2.9E-1}$	$8.4E-{01}_{4.4E-1}$	$1.8E{00}_{4.9E-1}$	$1.8E{00}_{3.4E-1}$	$9.8E-{01}_{4.1E-1}$	$1.7E{00}_{5.4E-1}$
Fpppp-64-334-1-1	$4.8E{00}_{1.3E0}$	$4.2E{00}_{1.4E0}$	$5.7E{00}_{1.5E0}$	$4.1E{00}_{1.8E0}$	$6.3E{00}_{1.8E0}$	$6.7E{00}_{1.3E0}$	$4.2E{00}_{1.5E0}$	$5.8E{00}_{1.6E0}$
Fpppp-64-334-1-5	$1.8E{00}_{7.4E-1}$	$1.5E{00}_{7.2E-1}$	$2.3E{00}_{6.2E-1}$	$1.2E{00}_{6.0E-1}$	$2.3E{00}_{6.6E-1}$	$2.4E{00}_{7.5E-1}$	$1.7E{00}_{6.4E-1}$	$1.9E{00}_{7.3E-1}$
Fpppp-64-334-1-10	$2.5E{00}_{1.1E0}$	$3.0E{00}_{9.4E-1}$	$3.2E{00}_{1.2E0}$	$2.4E{00}_{1.0E0}$	$3.2E{00}_{1.3E0}$	$3.8E{00}_{1.2E0}$	$2.6E{00}_{1.1E0}$	$3.2E{00}_{1.3E0}$

,

Table 8. IGD+ mean and standard deviation of the eight algorithms over 30 independent runs. Dark/light gray emphasize the best/second-best results.

Problem	AGEMOEA	AGEMOEA2	GWASFGA	MOCell	MOMBI	MOMBI2	NSGA2	SMS-EMOA
LIGO-8-76-0.1-0.1	$2.1E-{01}_{7.1E-2}$	$2.3E-{01}_{5.5E-2}$	$3.8E-{01}_{7.4E-2}$	$1.3E-{01}_{4.1E-2}$	$3.4E-{01}_{6.7E-2}$	$3.7E-{01}_{9.2E-2}$	$2.2E-{01}_{5.8E-2}$	$3.1E-{01}_{7.6E-2}$
LIGO-8-76-0.1-0.5	$7.0E-{01}_{2.5E-1}$	$6.2E-{01}_{2.0E-1}$	$1.2E{00}_{3.0E-1}$	$4.8E-{01}_{2.4E-1}$	$1.1E{00}_{2.5E-1}$	$1.2E{00}_{2.7E-1}$	$6.8E-{01}_{2.3E-1}$	$1.0E{00}_{2.9E-1}$
LIGO-8-76-0.1-1	$3.8E-{01}_{1.7E-1}$	$3.9E-{01}_{1.7E-1}$	$7.1E-{01}_{1.6E-1}$	$2.7E-{01}_{1.6E-1}$	$6.6E-{01}_{1.6E-1}$	$7.1E-{01}_{1.8E-1}$	$3.5E-{01}_{1.2E-1}$	$6.3E-{01}_{1.4E-1}$
LIGO-8-76-0.1-5	$2.3E{00}_{9.4E-1}$	$2.5E{00}_{8.5E-1}$	$3.3E{00}_{8.5E-1}$	$2.4E{00}_{1.3E0}$	$3.4E{00}_{6.8E-1}$	$3.3E{00}_{8.4E-1}$	$2.4E{00}_{1.0E0}$	$3.1E{00}_{8.9E-1}$
LIGO-8-76-0.1-10	$1.7E{00}_{6.7E-1}$	$2.0E{00}_{7.8E-1}$	$2.3E{00}_{8.5E-1}$	$1.7E{00}_{1.1E0}$	$2.4E{00}_{7.4E-1}$	$2.4E{00}_{8.0E-1}$	$1.9E{00}_{9.4E-1}$	$2.2E{00}_{7.1E-1}$
LIGO-8-76-0.25-0.1	$3.8E-{01}_{9.2E-2}$	$3.5E-{01}_{8.0E-2}$	$6.6E-{01}_{9.4E-2}$	$2.7E-{01}_{1.0E-1}$	$5.5E-{01}_{1.2E-1}$	$6.0E-{01}_{8.3E-2}$	$3.9E-{01}_{8.3E-2}$	$5.4E-{01}_{9.7E-2}$
LIGO-8-76-0.25-0.5	$2.4E-{01}_{7.2E-2}$	$2.2E-{01}_{9.3E-2}$	$3.9E-{01}_{7.9E-2}$	$1.6E-{01}_{6.8E-2}$	$3.3E-{01}_{7.3E-2}$	$3.3E-{01}_{9.0E-2}$	$2.4E-{01}_{6.7E-2}$	$2.8E-{01}_{8.6E-2}$
LIGO-8-76-0.25-1	$1.6E-{01}_{6.1E-2}$	$1.5E-{01}_{5.6E-2}$	$2.7E-{01}_{5.0E-2}$	$1.2E-{01}_{3.9E-2}$	$2.3E-{01}_{5.3E-2}$	$2.3E-{01}_{5.6E-2}$	$1.4E-{01}_{4.8E-2}$	$2.0E-{01}_{4.7E-2}$
LIGO-8-76-0.25-5	$4.2E-{01}_{1.9E-1}$	$4.3E-{01}_{1.4E-1}$	$7.4E-{01}_{2.1E-1}$	$3.9E-{01}_{1.8E-1}$	$6.4E-{01}_{1.8E-1}$	$7.0E-{01}_{1.6E-1}$	$4.3E-{01}_{2.2E-1}$	$5.6E-{01}_{1.9E-1}$
LIGO-8-76-0.25-10	$1.8E{00}_{7.7E-1}$	$1.9E{00}_{7.0E-1}$	$2.6E{00}_{6.5E-1}$	$1.8E{00}_{8.8E-1}$	$2.7E{00}_{7.1E-1}$	$2.8E{00}_{6.9E-1}$	$1.9E{00}_{7.9E-1}$	$2.3E{00}_{7.4E-1}$
LIGO-8-76-0.5-0.1	$2.2E-{01}_{7.5E-2}$	$2.0E-{01}_{7.1E-2}$	$4.5E-{01}_{9.7E-2}$	$1.6E-{01}_{8.3E-2}$	$3.6E-{01}_{8.1E-2}$	$3.7E-{01}_{1.0E-1}$	$1.9E-{01}_{7.4E-2}$	$3.0E-{01}_{7.5E-2}$
LIGO-8-76-0.5-0.5	$5.4E-{01}_{1.8E-1}$	$5.2E-{01}_{2.1E-1}$	$8.8E-{01}_{1.9E-1}$	$3.6E-{01}_{1.6E-1}$	$7.9E-{01}_{1.8E-1}$	$8.1E-{01}_{2.3E-1}$	$4.8E-{01}_{1.8E-1}$	$7.5E-{01}_{2.2E-1}$
LIGO-8-76-0.5-1	$7.6E-{01}_{2.9E-1}$	$8.4E-{01}_{3.2E-1}$	$1.4E{00}_{3.7E-1}$	$6.4E-{01}_{2.1E-1}$	$1.2E{00}_{3.0E-1}$	$1.3E{00}_{3.1E-1}$	$8.0E-{01}_{3.1E-1}$	$1.0E{00}_{2.9E-1}$
LIGO-8-76-0.5-5	$5.6E-{01}_{1.9E-1}$	$5.1E-{01}_{1.9E-1}$	$8.6E-{01}_{2.5E-1}$	$5.2E-{01}_{1.6E-1}$	$8.0E-{01}_{1.9E-1}$	$8.0E-{01}_{2.5E-1}$	$5.9E-{01}_{2.2E-1}$	$6.8E-{01}_{1.8E-1}$
LIGO-8-76-0.5-10	$5.8E-{01}_{1.8E-1}$	$6.1E-{01}_{2.5E-1}$	$8.9E-{01}_{2.0E-1}$	$5.8E-{01}_{2.7E-1}$	$8.6E-{01}_{2.2E-1}$	$8.5E-{01}_{2.4E-1}$	$5.7E-{01}_{2.5E-1}$	$7.4E-{01}_{2.3E-1}$
LIGO-8-76-0.75-0.1	$3.0E-{01}_{9.7E-2}$	$3.3E-{01}_{9.2E-2}$	$4.8E-{01}_{1.1E-1}$	$2.9E-{01}_{6.6E-2}$	$4.5E-{01}_{9.0E-2}$	$4.2E-{01}_{9.4E-2}$	$3.2E-{01}_{8.8E-2}$	$4.0E-{01}_{1.2E-1}$
LIGO-8-76-0.75-0.5	$6.6E-{01}_{2.9E-1}$	$6.2E-{01}_{2.8E-1}$	$1.3E{00}_{2.9E-1}$	$5.7E-{01}_{2.4E-1}$	$1.2E{00}_{4.1E-1}$	$1.1E{00}_{3.7E-1}$	$7.4E-{01}_{2.5E-1}$	$9.5E-{01}_{3.4E-1}$
LIGO-8-76-0.75-1	$6.7E-{01}_{2.3E-1}$	$6.7E-{01}_{3.0E-1}$	$1.2E{00}_{2.6E-1}$	$5.7E-{01}_{2.3E-1}$	$9.2E-{01}_{2.3E-1}$	$1.1E{00}_{2.6E-1}$	$6.7E-{01}_{2.0E-1}$	$9.2E-{01}_{2.4E-1}$
LIGO-8-76-0.75-5	$4.9E-{01}_{2.1E-1}$	$4.6E-{01}_{2.1E-1}$	$8.0E-{01}_{2.5E-1}$	$4.6E-{01}_{2.0E-1}$	$9.0E-{01}_{2.5E-1}$	$7.3E-{01}_{2.4E-1}$	$5.7E-{01}_{2.5E-1}$	$8.3E-{01}_{2.0E-1}$
LIGO-8-76-0.75-10	$8.7E-{01}_{3.6E-1}$	$9.1E-{01}_{3.9E-1}$	$1.1E{00}_{3.5E-1}$	$9.4E-{01}_{3.8E-1}$	$1.2E{00}_{3.8E-1}$	$1.2E{00}_{3.6E-1}$	$9.1E-{01}_{3.1E-1}$	$1.0E{00}_{3.0E-1}$
LIGO-8-76-1-0.1	$1.6E-{01}_{6.4E-2}$	$1.7E-{01}_{6.9E-2}$	$2.3E-{01}_{6.4E-2}$	$1.8E-{01}_{6.1E-2}$	$2.0E-{01}_{6.4E-2}$	$2.3E-{01}_{6.6E-2}$	$1.6E-{01}_{6.2E-2}$	$2.2E-{01}_{4.2E-2}$
LIGO-8-76-1-0.5	$3.2E-{01}_{1.1E-1}$	$3.1E-{01}_{1.2E-1}$	$5.8E-{01}_{1.4E-1}$	$3.9E-{01}_{1.2E-1}$	$4.4E-{01}_{1.2E-1}$	$5.2E-{01}_{1.2E-1}$	$3.3E-{01}_{1.4E-1}$	$4.0E-{01}_{1.1E-1}$
LIGO-8-76-1-1	$3.3E-{01}_{1.4E-1}$	$3.8E-{01}_{1.3E-1}$	$5.5E-{01}_{1.4E-1}$	$3.4E-{01}_{1.2E-1}$	$5.0E-{01}_{1.4E-1}$	$5.3E-{01}_{1.4E-1}$	$3.1E-{01}_{1.1E-1}$	$3.9E-{01}_{1.1E-1}$
LIGO-8-76-1-5	$3.7E-{01}_{1.3E-1}$	$4.0E-{01}_{1.6E-1}$	$5.2E-{01}_{2.0E-1}$	$3.6E-{01}_{1.3E-1}$	$5.5E-{01}_{1.4E-1}$	$5.6E-{01}_{1.4E-1}$	$3.9E-{01}_{1.3E-1}$	$5.1E-{01}_{1.5E-1}$
LIGO-8-76-1-10	$1.3E{00}_{3.3E-1}$	$1.1E{00}_{4.1E-1}$	$1.5E{00}_{3.3E-1}$	$1.1E{00}_{5.0E-1}$	$1.7E{00}_{4.7E-1}$	$1.5E{00}_{3.8E-1}$	$1.0E{00}_{4.8E-1}$	$1.7E{00}_{3.7E-1}$
LIGO-16-76-0.1-0.1	$4.5E-{01}_{1.3E-1}$	$4.8E-{01}_{1.3E-1}$	$8.9E-{01}_{1.5E-1}$	$2.8E-{01}_{9.4E-2}$	$7.0E-{01}_{1.7E-1}$	$7.7E-{01}_{1.1E-1}$	$4.6E-{01}_{1.3E-1}$	$6.7E-{01}_{1.1E-1}$
LIGO-16-76-0.1-0.5	$4.7E-{01}_{1.2E-1}$	$4.1E-{01}_{1.3E-1}$	$8.7E-{01}_{1.5E-1}$	$2.8E-{01}_{1.2E-1}$	$7.2E-{01}_{1.1E-1}$	$7.4E-{01}_{1.4E-1}$	$4.7E-{01}_{1.2E-1}$	$6.8E-{01}_{1.4E-1}$
LIGO-16-76-0.1-1	$2.1E-{01}_{9.0E-2}$	$2.2E-{01}_{7.3E-2}$	$4.5E-{01}_{8.5E-2}$	$1.4E-{01}_{6.1E-2}$	$3.5E-{01}_{7.7E-2}$	$3.6E-{01}_{9.7E-2}$	$2.2E-{01}_{9.7E-2}$	$2.7E-{01}_{8.2E-2}$
LIGO-16-76-0.1-5	$4.7E-{01}_{1.9E-1}$	$5.2E-{01}_{1.8E-1}$	$8.0E-{01}_{1.9E-1}$	$3.5E-{01}_{1.9E-1}$	$7.4E-{01}_{1.6E-1}$	$7.8E-{01}_{2.0E-1}$	$4.8E-{01}_{1.4E-1}$	$6.4E-{01}_{1.8E-1}$
LIGO-16-76-0.1-10	$5.5E-{01}_{2.1E-1}$	$5.2E-{01}_{2.5E-1}$	$9.7E-{01}_{2.7E-1}$	$3.6E-{01}_{1.7E-1}$	$8.7E-{01}_{2.3E-1}$	$9.1E-{01}_{1.9E-1}$	$5.1E-{01}_{1.7E-1}$	$7.9E-{01}_{2.6E-1}$
LIGO-16-76-0.25-0.1	$7.2E-{01}_{2.8E-1}$	$7.2E-{01}_{2.2E-1}$	$1.3E{00}_{3.2E-1}$	$4.5E-{01}_{2.2E-1}$	$1.2E{00}_{3.1E-1}$	$1.3E{00}_{3.6E-1}$	$7.6E-{01}_{2.5E-1}$	$1.1E{00}_{2.0E-1}$
LIGO-16-76-0.25-0.5	$7.3E-{01}_{1.8E-1}$	$6.7E-{01}_{2.1E-1}$	$1.3E{00}_{2.4E-1}$	$4.9E-{01}_{1.8E-1}$	$9.7E-{01}_{1.9E-1}$	$9.8E-{01}_{1.9E-1}$	$7.4E-{01}_{2.1E-1}$	$9.5E-{01}_{1.7E-1}$
LIGO-16-76-0.25-1	$2.1E{00}_{6.3E-1}$	$2.1E{00}_{7.2E-1}$	$3.0E{00}_{5.3E-1}$	$1.4E{00}_{8.1E-1}$	$2.8E{00}_{6.0E-1}$	$2.3E{00}_{5.8E-1}$	$2.1E{00}_{8.6E-1}$	$2.7E{00}_{6.4E-1}$
LIGO-16-76-0.25-5	$1.9E{00}_{5.1E-1}$	$1.8E{00}_{7.1E-1}$	$3.2E{00}_{9.0E-1}$	$1.1E{00}_{5.4E-1}$	$2.8E{00}_{5.4E-1}$	$2.8E{00}_{6.9E-1}$	$1.7E{00}_{6.8E-1}$	$2.4E{00}_{6.2E-1}$
LIGO-16-76-0.25-10	$8.2E-{01}_{3.6E-1}$	$7.7E-{01}_{3.2E-1}$	$1.4E{00}_{3.9E-1}$	$6.1E-{01}_{3.3E-1}$	$1.2E{00}_{3.3E-1}$	$1.3E{00}_{3.4E-1}$	$8.6E-{01}_{3.7E-1}$	$1.2E{00}_{3.6E-1}$
LIGO-16-76-0.5-0.1	$2.6E-{01}_{1.1E-1}$	$2.4E-{01}_{5.7E-2}$	$4.1E-{01}_{1.1E-1}$	$1.4E-{01}_{7.2E-2}$	$3.6E-{01}_{9.1E-2}$	$3.6E-{01}_{6.8E-2}$	$2.3E-{01}_{6.7E-2}$	$3.2E-{01}_{9.3E-2}$
LIGO-16-76-0.5-0.5	$5.3E-{01}_{1.7E-1}$	$4.5E-{01}_{1.5E-1}$	$8.8E-{01}_{2.0E-1}$	$2.4E-{01}_{1.4E-1}$	$7.0E-{01}_{1.5E-1}$	$6.4E-{01}_{1.2E-1}$	$4.8E-{01}_{1.5E-1}$	$6.3E-{01}_{1.5E-1}$
LIGO-16-76-0.5-1	$3.6E-{01}_{1.1E-1}$	$3.6E-{01}_{1.2E-1}$	$5.9E-{01}_{9.9E-2}$	$2.2E-{01}_{1.1E-1}$	$5.4E-{01}_{1.1E-1}$	$4.8E-{01}_{1.2E-1}$	$3.5E-{01}_{1.0E-1}$	$4.6E-{01}_{1.1E-1}$
LIGO-16-76-0.5-5	$1.1E{01}_{2.8E0}$	$1.2E{01}_{3.4E0}$	$1.6E{01}_{3.9E0}$	$8.9E{00}_{3.8E0}$	$1.4E{01}_{3.0E0}$	$1.4E{01}_{2.8E0}$	$1.2E{01}_{2.7E0}$	$1.4E{01}_{3.0E0}$
LIGO-16-76-0.5-10	$8.4E-{01}_{3.7E-1}$	$9.2E-{01}_{3.3E-1}$	$1.5E{00}_{4.2E-1}$	$4.6E-{01}_{2.7E-1}$	$1.3E{00}_{3.2E-1}$	$1.3E{00}_{3.4E-1}$	$8.4E-{01}_{3.2E-1}$	$1.3E{00}_{3.4E-1}$
LIGO-16-76-0.75-0.1	$3.3E-{01}_{9.1E-2}$	$3.2E-{01}_{1.1E-1}$	$6.3E-{01}_{1.1E-1}$	$2.3E-{01}_{1.2E-1}$	$4.9E-{01}_{1.1E-1}$	$5.3E-{01}_{1.0E-1}$	$3.0E-{01}_{9.3E-2}$	$4.6E-{01}_{1.3E-1}$
LIGO-16-76-0.75-0.5	$7.7E-{01}_{2.5E-1}$	$7.2E-{01}_{2.8E-1}$	$1.2E{00}_{3.0E-1}$	$4.5E-{01}_{2.5E-1}$	$1.0E{00}_{2.2E-1}$	$1.0E{00}_{2.8E-1}$	$6.7E-{01}_{2.6E-1}$	$9.5E-{01}_{2.2E-1}$
LIGO-16-76-0.75-1	$4.2E-{01}_{1.2E-1}$	$4.1E-{01}_{1.6E-1}$	$7.5E-{01}_{1.7E-1}$	$3.4E-{01}_{1.2E-1}$	$6.0E-{01}_{1.5E-1}$	$6.6E-{01}_{1.4E-1}$	$4.1E-{01}_{1.3E-1}$	$5.4E-{01}_{1.4E-1}$
LIGO-16-76-0.75-5	$3.1E{00}_{7.7E-1}$	$3.2E{00}_{6.5E-1}$	$4.6E{00}_{1.2E0}$	$2.2E{00}_{9.9E-1}$	$4.5E{00}_{1.3E0}$	$4.5E{00}_{1.0E0}$	$3.2E{00}_{8.1E-1}$	$4.0E{00}_{7.0E-1}$
LIGO-16-76-0.75-10	$3.0E{00}_{9.3E-1}$	$2.9E{00}_{1.5E0}$	$4.8E{00}_{1.2E0}$	$1.8E{00}_{1.0E0}$	$4.8E{00}_{1.3E0}$	$4.5E{00}_{1.2E0}$	$2.9E{00}_{1.0E0}$	$3.9E{00}_{1.2E0}$
LIGO-16-76-1-0.1	$3.1E-{01}_{1.0E-1}$	$3.2E-{01}_{1.4E-1}$	$6.0E-{01}_{1.7E-1}$	$2.3E-{01}_{1.1E-1}$	$4.3E-{01}_{1.3E-1}$	$5.0E-{01}_{1.3E-1}$	$3.0E-{01}_{1.3E-1}$	$4.0E-{01}_{1.4E-1}$
LIGO-16-76-1-0.5	$3.4E-{01}_{1.4E-1}$	$3.2E-{01}_{1.3E-1}$	$5.7E-{01}_{1.2E-1}$	$2.8E-{01}_{1.1E-1}$	$5.0E-{01}_{1.4E-1}$	$5.0E-{01}_{1.4E-1}$	$2.7E-{01}_{1.1E-1}$	$3.9E-{01}_{1.4E-1}$
LIGO-16-76-1-1	$6.4E-{01}_{2.0E-1}$	$6.4E-{01}_{2.4E-1}$	$9.8E-{01}_{3.1E-1}$	$4.2E-{01}_{1.8E-1}$	$8.5E-{01}_{2.3E-1}$	$7.5E-{01}_{1.9E-1}$	$5.9E-{01}_{2.1E-1}$	$7.3E-{01}_{1.8E-1}$
LIGO-16-76-1-5	$4.6E-{01}_{1.9E-1}$	$4.2E-{01}_{1.7E-1}$	$7.0E-{01}_{2.1E-1}$	$2.8E-{01}_{1.2E-1}$	$6.4E-{01}_{1.3E-1}$	$6.2E-{01}_{1.6E-1}$	$4.4E-{01}_{2.1E-1}$	$5.5E-{01}_{1.7E-1}$
LIGO-16-76-1-10	$5.1E{00}_{1.9E0}$	$5.2E{00}_{2.2E0}$	$7.8E{00}_{1.8E0}$	$3.9E{00}_{2.1E0}$	$7.6E{00}_{2.0E0}$	$8.5E{00}_{1.9E0}$	$5.4E{00}_{2.0E0}$	$6.5E{00}_{1.8E0}$
LIGO-32-76-0.1-0.1	$6.8E-{01}_{2.4E-1}$	$7.8E-{01}_{2.0E-1}$	$1.6E{00}_{3.4E-1}$	$5.0E-{01}_{2.3E-1}$	$1.5E{00}_{3.8E-1}$	$2.3E{00}_{4.5E-1}$	$8.7E-{01}_{3.1E-1}$	$1.2E{00}_{2.9E-1}$
LIGO-32-76-0.1-0.5	$1.8E-{01}_{6.6E-2}$	$2.3E-{01}_{7.2E-2}$	$4.2E-{01}_{8.2E-2}$	$1.2E-{01}_{5.9E-2}$	$3.4E-{01}_{9.0E-2}$	$4.1E-{01}_{9.3E-2}$	$2.1E-{01}_{8.3E-2}$	$3.1E-{01}_{7.1E-2}$
LIGO-32-76-0.1-1	$2.9E-{01}_{1.2E-1}$	$3.1E-{01}_{1.0E-1}$	$5.9E-{01}_{1.7E-1}$	$2.1E-{01}_{9.2E-2}$	$4.9E-{01}_{9.9E-2}$	$4.8E-{01}_{1.2E-1}$	$3.4E-{01}_{1.1E-1}$	$4.4E-{01}_{1.3E-1}$
LIGO-32-76-0.1-5	$1.2E{00}_{3.8E-1}$	$9.4E-{01}_{4.3E-1}$	$1.8E{00}_{4.7E-1}$	$6.7E-{01}_{3.6E-1}$	$1.5E{00}_{4.9E-1}$	$2.0E{00}_{4.3E-1}$	$1.0E{00}_{4.1E-1}$	$1.3E{00}_{4.1E-1}$
LIGO-32-76-0.1-10	$8.6E{00}_{2.5E0}$	$9.6E{00}_{2.4E0}$	$1.3E{01}_{3.3E0}$	$4.8E{00}_{2.3E0}$	$1.3E{01}_{2.3E0}$	$1.2E{01}_{2.5E0}$	$8.9E{00}_{2.7E0}$	$1.1E{01}_{2.9E0}$
LIGO-32-76-0.25-0.1	$3.6E-{01}_{1.2E-1}$	$3.1E-{01}_{1.0E-1}$	$7.1E-{01}_{1.4E-1}$	$2.2E-{01}_{1.1E-1}$	$6.0E-{01}_{1.2E-1}$	$8.8E-{01}_{1.5E-1}$	$3.7E-{01}_{1.0E-1}$	$5.1E-{01}_{1.2E-1}$
LIGO-32-76-0.25-0.5	$2.8E-{01}_{8.2E-2}$	$2.9E-{01}_{9.7E-2}$	$4.8E-{01}_{1.0E-1}$	$1.7E-{01}_{7.6E-2}$	$4.5E-{01}_{8.6E-2}$	$4.8E-{01}_{9.9E-2}$	$2.7E-{01}_{5.8E-2}$	$3.4E-{01}_{9.2E-2}$
LIGO-32-76-0.25-1	$8.3E-{01}_{3.8E-1}$	$8.0E-{01}_{3.4E-1}$	$1.1E{00}_{4.0E-1}$	$5.0E-{01}_{2.7E-1}$	$1.2E{00}_{3.9E-1}$	$6.9E-{01}_{2.3E-1}$	$7.2E-{01}_{2.6E-1}$	$1.0E{00}_{3.8E-1}$
LIGO-32-76-0.25-5	$6.2E-{01}_{2.0E-1}$	$7.2E-{01}_{2.4E-1}$	$1.1E{00}_{2.2E-1}$	$3.6E-{01}_{1.7E-1}$	$9.3E-{01}_{1.7E-1}$	$9.1E-{01}_{1.8E-1}$	$7.0E-{01}_{2.0E-1}$	$8.7E-{01}_{2.2E-1}$
LIGO-32-76-0.25-10	$6.1E-{01}_{1.8E-1}$	$6.6E-{01}_{2.2E-1}$	$9.5E-{01}_{2.4E-1}$	$4.4E-{01}_{2.1E-1}$	$9.8E-{01}_{2.3E-1}$	$1.1E{00}_{2.9E-1}$	$6.5E-{01}_{2.7E-1}$	$7.3E-{01}_{2.0E-1}$
LIGO-32-76-0.5-0.1	$2.4E-{01}_{9.6E-2}$	$2.6E-{01}_{7.4E-2}$	$4.6E-{01}_{9.0E-2}$	$1.6E-{01}_{6.9E-2}$	$4.2E-{01}_{1.1E-1}$	$5.3E-{01}_{9.0E-2}$	$2.4E-{01}_{8.1E-2}$	$3.3E-{01}_{8.6E-2}$
LIGO-32-76-0.5-0.5	$2.6E-{01}_{8.5E-2}$	$3.1E-{01}_{1.0E-1}$	$5.1E-{01}_{1.3E-1}$	$1.6E-{01}_{5.7E-2}$	$4.5E-{01}_{8.1E-2}$	$4.6E-{01}_{8.9E-2}$	$2.6E-{01}_{9.1E-2}$	$3.7E-{01}_{1.0E-1}$
LIGO-32-76-0.5-1	$4.9E{01}_{1.4E1}$	$4.6E{01}_{1.3E1}$	$8.6E{01}_{1.6E1}$	$3.8E{01}_{1.7E1}$	$7.5E{01}_{1.9E1}$	$1.0E{02}_{2.2E1}$	$5.4E{01}_{1.3E1}$	$5.9E{01}_{1.8E1}$
LIGO-32-76-0.5-5	$3.6E{00}_{9.8E-1}$	$3.7E{00}_{1.9E0}$	$6.7E{00}_{1.6E0}$	$2.3E{00}_{1.1E0}$	$5.1E{00}_{1.8E0}$	$7.6E{00}_{2.3E0}$	$3.7E{00}_{1.2E0}$	$4.8E{00}_{1.6E0}$
LIGO-32-76-0.5-10	$1.0E{00}_{3.1E-1}$	$1.0E{00}_{3.4E-1}$	$1.9E{00}_{5.3E-1}$	$5.5E-{01}_{3.4E-1}$	$1.7E{00}_{4.8E-1}$	$2.2E{00}_{5.7E-1}$	$1.1E{00}_{4.0E-1}$	$1.3E{00}_{3.7E-1}$
LIGO-32-76-0.75-0.1	$4.4E-{01}_{1.1E-1}$	$4.1E-{01}_{9.3E-2}$	$6.5E-{01}_{1.7E-1}$	$2.5E-{01}_{1.2E-1}$	$5.9E-{01}_{1.1E-1}$	$5.2E-{01}_{1.1E-1}$	$4.2E-{01}_{1.3E-1}$	$5.8E-{01}_{8.3E-2}$
LIGO-32-76-0.75-0.5	$2.4E-{01}_{7.2E-2}$	$2.5E-{01}_{6.3E-2}$	$4.7E-{01}_{1.2E-1}$	$1.5E-{01}_{8.7E-2}$	$3.9E-{01}_{7.5E-2}$	$3.7E-{01}_{7.5E-2}$	$2.7E-{01}_{7.4E-2}$	$3.4E-{01}_{6.5E-2}$
LIGO-32-76-0.75-1	$5.2E-{01}_{1.5E-1}$	$5.7E-{01}_{2.3E-1}$	$8.2E-{01}_{2.0E-1}$	$3.9E-{01}_{1.7E-1}$	$8.1E-{01}_{1.4E-1}$	$7.2E-{01}_{1.6E-1}$	$5.6E-{01}_{1.8E-1}$	$6.8E-{01}_{1.5E-1}$
LIGO-32-76-0.75-5	$1.2E{00}_{3.2E-1}$	$1.1E{00}_{3.1E-1}$	$1.9E{00}_{4.6E-1}$	$6.3E-{01}_{3.0E-1}$	$1.7E{00}_{3.9E-1}$	$1.5E{00}_{3.1E-1}$	$1.1E{00}_{3.1E-1}$	$1.5E{00}_{2.7E-1}$
LIGO-32-76-0.75-10	$9.1E-{01}_{3.1E-1}$	$8.2E-{01}_{3.0E-1}$	$1.5E{00}_{3.7E-1}$	$4.8E-{01}_{2.1E-1}$	$1.5E{00}_{3.1E-1}$	$1.6E{00}_{3.1E-1}$	$8.4E-{01}_{3.4E-1}$	$1.3E{00}_{3.4E-1}$
LIGO-32-76-1-0.1	$2.0E-{01}_{5.3E-2}$	$2.3E-{01}_{8.4E-2}$	$3.3E-{01}_{8.7E-2}$	$1.2E-{01}_{4.1E-2}$	$2.9E-{01}_{7.1E-2}$	$2.6E-{01}_{6.0E-2}$	$1.9E-{01}_{8.5E-2}$	$2.5E-{01}_{7.5E-2}$
LIGO-32-76-1-0.5	$1.7E-{01}_{8.7E-2}$	$1.5E-{01}_{6.3E-2}$	$3.7E-{01}_{6.8E-2}$	$1.8E-{01}_{7.6E-2}$	$3.2E-{01}_{9.2E-2}$	$3.7E-{01}_{1.3E-1}$	$1.8E-{01}_{6.5E-2}$	$2.6E-{01}_{9.5E-2}$
LIGO-32-76-1-1	$2.9E-{01}_{8.1E-2}$	$3.0E-{01}_{1.1E-1}$	$4.9E-{01}_{1.0E-1}$	$2.2E-{01}_{8.2E-2}$	$4.2E-{01}_{9.4E-2}$	$4.3E-{01}_{1.1E-1}$	$2.8E-{01}_{9.7E-2}$	$3.4E-{01}_{8.3E-2}$
LIGO-32-76-1-5	$1.1E{00}_{4.2E-1}$	$1.2E{00}_{3.8E-1}$	$2.1E{00}_{6.5E-1}$	$7.6E-{01}_{3.2E-1}$	$1.8E{00}_{5.0E-1}$	$1.8E{00}_{4.6E-1}$	$1.1E{00}_{4.3E-1}$	$1.6E{00}_{4.6E-1}$
LIGO-32-76-1-10	$1.1E{01}_{2.8E0}$	$1.1E{01}_{2.8E0}$	$1.6E{01}_{3.8E0}$	$7.5E{00}_{3.0E0}$	$1.5E{01}_{3.6E0}$	$1.3E{01}_{2.5E0}$	$1.0E{01}_{2.1E0}$	$1.3E{01}_{3.2E0}$
LIGO-64-76-0.1-0.1	$7.4E-{02}_{2.4E-2}$	$7.9E-{02}_{2.5E-2}$	$9.7E-{02}_{2.7E-2}$	$4.5E-{02}_{2.0E-2}$	$9.8E-{02}_{2.1E-2}$	$9.0E-{02}_{1.6E-2}$	$7.3E-{02}_{2.5E-2}$	$9.4E-{02}_{2.6E-2}$
LIGO-64-76-0.1-0.5	$7.6E-{01}_{2.1E-1}$	$7.3E-{01}_{2.7E-1}$	$7.1E-{01}_{2.7E-1}$	$5.2E-{01}_{2.0E-1}$	$8.8E-{01}_{3.0E-1}$	$3.7E-{01}_{1.9E-1}$	$7.0E-{01}_{2.2E-1}$	$9.0E-{01}_{3.1E-1}$
LIGO-64-76-0.1-1	$5.6E-{01}_{1.7E-1}$	$5.9E-{01}_{1.6E-1}$	$9.9E-{01}_{2.4E-1}$	$3.7E-{01}_{2.0E-1}$	$9.2E-{01}_{1.6E-1}$	$1.1E{00}_{2.2E-1}$	$5.6E-{01}_{1.8E-1}$	$7.0E-{01}_{1.8E-1}$
LIGO-64-76-0.1-5	$5.0E{00}_{1.7E0}$	$4.8E{00}_{2.0E0}$	$6.6E{00}_{1.6E0}$	$3.8E{00}_{1.6E0}$	$6.5E{00}_{1.2E0}$	$9.7E{00}_{2.3E0}$	$4.6E{00}_{1.6E0}$	$4.6E{00}_{1.6E0}$
LIGO-64-76-0.1-10	$2.7E{00}_{8.8E-1}$	$2.9E{00}_{7.9E-1}$	$3.8E{00}_{1.3E0}$	$1.7E{00}_{8.3E-1}$	$4.4E{00}_{8.2E-1}$	$2.7E{00}_{7.1E-1}$	$2.7E{00}_{7.5E-1}$	$3.7E{00}_{1.1E0}$
LIGO-64-76-0.25-0.1	$1.9E-{01}_{6.9E-2}$	$1.8E-{01}_{5.0E-2}$	$2.7E-{01}_{7.4E-2}$	$1.2E-{01}_{5.3E-2}$	$2.6E-{01}_{6.3E-2}$	$1.8E-{01}_{3.3E-2}$	$1.7E-{01}_{5.9E-2}$	$2.7E-{01}_{6.9E-2}$
LIGO-64-76-0.25-0.5	$1.8E-{01}_{6.4E-2}$	$1.8E-{01}_{5.2E-2}$	$2.6E-{01}_{5.9E-2}$	$1.3E-{01}_{4.8E-2}$	$2.4E-{01}_{3.7E-2}$	$2.4E-{01}_{3.0E-2}$	$1.7E-{01}_{5.6E-2}$	$2.1E-{01}_{4.6E-2}$
LIGO-64-76-0.25-1	$2.5E-{01}_{1.2E-1}$	$2.4E-{01}_{9.9E-2}$	$4.6E-{01}_{1.3E-1}$	$1.9E-{01}_{8.6E-2}$	$4.4E-{01}_{1.3E-1}$	$6.7E-{01}_{1.6E-1}$	$2.5E-{01}_{8.9E-2}$	$3.2E-{01}_{1.1E-1}$
LIGO-64-76-0.25-5	$3.0E-{01}_{1.4E-1}$	$3.4E-{01}_{1.0E-1}$	$5.8E-{01}_{1.5E-1}$	$2.9E-{01}_{1.5E-1}$	$6.1E-{01}_{1.5E-1}$	$8.6E-{01}_{2.1E-1}$	$4.0E-{01}_{1.7E-1}$	$4.1E-{01}_{1.4E-1}$
LIGO-64-76-0.25-10	$2.8E{00}_{7.7E-1}$	$2.8E{00}_{8.9E-1}$	$4.8E{00}_{1.0E0}$	$2.3E{00}_{9.4E-1}$	$4.4E{00}_{1.1E0}$	$5.4E{00}_{1.5E0}$	$3.0E{00}_{9.4E-1}$	$3.6E{00}_{9.9E-1}$
LIGO-64-76-0.5-0.1	$2.9E-{01}_{6.6E-2}$	$2.6E-{01}_{7.2E-2}$	$5.9E-{01}_{1.2E-1}$	$1.8E-{01}_{8.4E-2}$	$4.9E-{01}_{1.3E-1}$	$9.7E-{01}_{2.0E-1}$	$2.8E-{01}_{9.8E-2}$	$3.6E-{01}_{6.4E-2}$
LIGO-64-76-0.5-0.5	$1.5E-{01}_{4.7E-2}$	$1.6E-{01}_{5.6E-2}$	$2.8E-{01}_{6.7E-2}$	$1.5E-{01}_{5.0E-2}$	$2.5E-{01}_{6.4E-2}$	$3.6E-{01}_{9.7E-2}$	$1.8E-{01}_{5.3E-2}$	$2.0E-{01}_{5.0E-2}$
LIGO-64-76-0.5-1	$2.2E-{01}_{9.9E-2}$	$2.0E-{01}_{9.8E-2}$	$3.8E-{01}_{1.1E-1}$	$1.9E-{01}_{9.5E-2}$	$3.5E-{01}_{1.0E-1}$	$6.3E-{01}_{1.7E-1}$	$2.2E-{01}_{8.2E-2}$	$2.5E-{01}_{9.6E-2}$
LIGO-64-76-0.5-5	$2.2E-{01}_{8.6E-2}$	$2.3E-{01}_{7.6E-2}$	$4.3E-{01}_{1.1E-1}$	$1.7E-{01}_{7.4E-2}$	$4.5E-{01}_{1.4E-1}$	$5.4E-{01}_{1.6E-1}$	$2.4E-{01}_{8.2E-2}$	$3.0E-{01}_{1.3E-1}$
LIGO-64-76-0.5-10	$1.1E{01}_{3.9E0}$	$1.2E{01}_{4.7E0}$	$1.8E{01}_{4.8E0}$	$7.8E{00}_{4.9E0}$	$1.5E{01}_{5.0E0}$	$2.4E{01}_{5.7E0}$	$1.1E{01}_{5.3E0}$	$1.2E{01}_{4.5E0}$
LIGO-64-76-0.75-0.1	$3.4E-{01}_{9.4E-2}$	$3.4E-{01}_{1.2E-1}$	$7.0E-{01}_{1.4E-1}$	$2.8E-{01}_{1.4E-1}$	$5.5E-{01}_{1.4E-1}$	$9.2E-{01}_{2.3E-1}$	$3.7E-{01}_{1.1E-1}$	$4.6E-{01}_{1.6E-1}$
LIGO-64-76-0.75-0.5	$8.6E-{02}_{2.8E-2}$	$8.7E-{02}_{3.7E-2}$	$1.5E-{01}_{3.2E-2}$	$6.1E-{02}_{2.2E-2}$	$1.2E-{01}_{2.7E-2}$	$1.4E-{01}_{3.2E-2}$	$8.3E-{02}_{2.4E-2}$	$1.1E-{01}_{2.8E-2}$
LIGO-64-76-0.75-1	$1.9E-{01}_{5.4E-2}$	$1.9E-{01}_{4.7E-2}$	$3.0E-{01}_{7.1E-2}$	$1.7E-{01}_{6.3E-2}$	$2.7E-{01}_{5.9E-2}$	$3.0E-{01}_{7.7E-2}$	$2.0E-{01}_{5.9E-2}$	$2.4E-{01}_{6.5E-2}$
LIGO-64-76-0.75-5	$4.0E-{01}_{1.3E-1}$	$4.5E-{01}_{1.5E-1}$	$6.6E-{01}_{1.8E-1}$	$3.9E-{01}_{1.9E-1}$	$6.8E-{01}_{2.2E-1}$	$8.6E-{01}_{2.5E-1}$	$5.0E-{01}_{1.5E-1}$	$5.5E-{01}_{1.5E-1}$
LIGO-64-76-0.75-10	$6.5E-{01}_{2.3E-1}$	$7.1E-{01}_{3.0E-1}$	$1.2E{00}_{3.4E-1}$	$4.6E-{01}_{2.5E-1}$	$1.2E{00}_{3.4E-1}$	$1.4E{00}_{3.2E-1}$	$8.0E-{01}_{2.2E-1}$	$1.0E{00}_{2.4E-1}$
LIGO-64-76-1-0.1	$1.2E-{01}_{4.0E-2}$	$1.2E-{01}_{3.8E-2}$	$2.0E-{01}_{4.3E-2}$	$8.0E-{02}_{2.5E-2}$	$1.8E-{01}_{4.6E-2}$	$1.6E-{01}_{3.6E-2}$	$1.2E-{01}_{3.2E-2}$	$1.7E-{01}_{4.5E-2}$
LIGO-64-76-1-0.5	$2.1E-{01}_{8.5E-2}$	$2.0E-{01}_{6.7E-2}$	$3.9E-{01}_{9.6E-2}$	$1.8E-{01}_{7.7E-2}$	$3.5E-{01}_{1.1E-1}$	$5.3E-{01}_{1.6E-1}$	$2.1E-{01}_{7.2E-2}$	$2.8E-{01}_{9.2E-2}$
LIGO-64-76-1-1	$1.8E-{01}_{8.7E-2}$	$1.7E-{01}_{5.8E-2}$	$3.1E-{01}_{8.7E-2}$	$1.5E-{01}_{5.5E-2}$	$3.0E-{01}_{9.0E-2}$	$4.0E-{01}_{8.3E-2}$	$1.9E-{01}_{6.9E-2}$	$2.1E-{01}_{7.1E-2}$
LIGO-64-76-1-5	$5.3E-{01}_{2.0E-1}$	$5.3E-{01}_{1.6E-1}$	$8.7E-{01}_{2.6E-1}$	$4.1E-{01}_{1.7E-1}$	$8.5E-{01}_{2.9E-1}$	$9.7E-{01}_{3.2E-1}$	$5.5E-{01}_{2.3E-1}$	$6.5E-{01}_{2.1E-1}$
LIGO-64-76-1-10	$3.1E{00}_{7.1E-1}$	$2.9E{00}_{8.3E-1}$	$4.4E{00}_{1.3E0}$	$2.2E{00}_{9.3E-1}$	$4.5E{00}_{8.9E-1}$	$5.6E{00}_{8.1E-1}$	$2.9E{00}_{9.8E-1}$	$3.5E{00}_{8.8E-1}$

,

Table 9. IGD+ mean and standard deviation of the eight algorithms over 30 independent runs. Dark/light gray emphasize the best/second-best results.

Problem	AGEMOEA	AGEMOEA2	GWASFGA	MOCell	MOMBI	MOMBI2	NSGA2	SMS-EMOA
Robot-8-88-0.1-0.1	$5.2E-{01}_{1.3E-1}$	$4.6E-{01}_{1.9E-1}$	$9.0E-{01}_{2.3E-1}$	$3.3E-{01}_{1.7E-1}$	$8.4E-{01}_{1.8E-1}$	$8.7E-{01}_{2.5E-1}$	$4.7E-{01}_{1.6E-1}$	$7.4E-{01}_{1.5E-1}$
Robot-8-88-0.1-0.5	$3.7E-{01}_{1.4E-1}$	$3.2E-{01}_{1.1E-1}$	$6.2E-{01}_{1.3E-1}$	$2.8E-{01}_{9.2E-2}$	$5.3E-{01}_{1.3E-1}$	$5.6E-{01}_{1.1E-1}$	$3.9E-{01}_{1.3E-1}$	$5.0E-{01}_{1.3E-1}$
Robot-8-88-0.1-1	$2.0E-{01}_{8.0E-2}$	$2.0E-{01}_{5.9E-2}$	$3.3E-{01}_{9.7E-2}$	$1.6E-{01}_{6.8E-2}$	$2.8E-{01}_{7.7E-2}$	$3.1E-{01}_{7.9E-2}$	$1.9E-{01}_{6.6E-2}$	$2.6E-{01}_{9.0E-2}$
Robot-8-88-0.1-5	$2.3E-{01}_{9.2E-2}$	$2.2E-{01}_{7.1E-2}$	$3.2E-{01}_{1.0E-1}$	$2.8E-{01}_{1.0E-1}$	$3.3E-{01}_{9.2E-2}$	$3.0E-{01}_{1.0E-1}$	$2.5E-{01}_{9.8E-2}$	$2.8E-{01}_{9.6E-2}$
Robot-8-88-0.1-10	$1.4E{00}_{7.1E-1}$	$1.7E{00}_{6.7E-1}$	$2.0E{00}_{6.8E-1}$	$2.1E{00}_{8.6E-1}$	$1.7E{00}_{6.2E-1}$	$2.0E{00}_{4.6E-1}$	$1.4E{00}_{5.6E-1}$	$1.7E{00}_{4.6E-1}$
Robot-8-88-0.25-0.1	$3.8E-{01}_{9.6E-2}$	$3.9E-{01}_{1.1E-1}$	$6.6E-{01}_{1.1E-1}$	$2.1E-{01}_{8.3E-2}$	$6.2E-{01}_{1.4E-1}$	$5.6E-{01}_{9.6E-2}$	$4.0E-{01}_{1.6E-1}$	$5.4E-{01}_{1.1E-1}$
Robot-8-88-0.25-0.5	$6.4E-{01}_{3.0E-1}$	$5.8E-{01}_{2.9E-1}$	$7.4E-{01}_{2.0E-1}$	$4.5E-{01}_{3.1E-1}$	$8.2E-{01}_{3.3E-1}$	$1.0E{00}_{2.7E-1}$	$5.5E-{01}_{3.6E-1}$	$8.0E-{01}_{2.8E-1}$
Robot-8-88-0.25-1	$1.0E{00}_{3.6E-1}$	$1.0E{00}_{4.8E-1}$	$1.8E{00}_{5.8E-1}$	$9.7E-{01}_{3.8E-1}$	$1.5E{00}_{4.2E-1}$	$1.8E{00}_{4.9E-1}$	$1.0E{00}_{3.5E-1}$	$1.4E{00}_{5.3E-1}$
Robot-8-88-0.25-5	$1.0E{00}_{4.6E-1}$	$9.1E-{01}_{3.9E-1}$	$1.4E{00}_{5.3E-1}$	$1.1E{00}_{4.3E-1}$	$1.4E{00}_{3.7E-1}$	$1.4E{00}_{4.0E-1}$	$1.1E{00}_{4.0E-1}$	$1.1E{00}_{4.1E-1}$
Robot-8-88-0.25-10	$1.1E{00}_{4.7E-1}$	$1.1E{00}_{4.8E-1}$	$1.9E{00}_{5.1E-1}$	$1.8E{00}_{6.4E-1}$	$1.7E{00}_{5.0E-1}$	$2.0E{00}_{5.5E-1}$	$1.3E{00}_{4.4E-1}$	$1.6E{00}_{6.0E-1}$
Robot-8-88-0.5-0.1	$4.0E-{01}_{9.5E-2}$	$4.1E-{01}_{1.1E-1}$	$6.1E-{01}_{1.4E-1}$	$2.9E-{01}_{1.2E-1}$	$5.4E-{01}_{1.2E-1}$	$6.0E-{01}_{1.5E-1}$	$3.9E-{01}_{1.1E-1}$	$5.2E-{01}_{1.2E-1}$
Robot-8-88-0.5-0.5	$4.6E-{01}_{1.7E-1}$	$3.9E-{01}_{1.6E-1}$	$7.3E-{01}_{1.7E-1}$	$3.6E-{01}_{1.4E-1}$	$6.7E-{01}_{2.0E-1}$	$6.0E-{01}_{2.0E-1}$	$4.7E-{01}_{1.2E-1}$	$5.9E-{01}_{1.6E-1}$
Robot-8-88-0.5-1	$2.2E-{01}_{7.4E-2}$	$2.2E-{01}_{7.5E-2}$	$3.1E-{01}_{6.7E-2}$	$1.9E-{01}_{5.9E-2}$	$2.9E-{01}_{8.4E-2}$	$2.8E-{01}_{6.1E-2}$	$2.1E-{01}_{7.4E-2}$	$2.6E-{01}_{6.3E-2}$
Robot-8-88-0.5-5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Robot-8-88-0.5-10	$2.6E{00}_{1.1E0}$	$2.6E{00}_{9.5E-1}$	$3.2E{00}_{1.1E0}$	$3.1E{00}_{1.2E0}$	$3.4E{00}_{1.2E0}$	$3.3E{00}_{8.3E-1}$	$2.4E{00}_{1.2E0}$	$2.7E{00}_{1.0E0}$
Robot-8-88-0.75-0.1	$2.7E-{01}_{8.4E-2}$	$2.8E-{01}_{9.0E-2}$	$4.3E-{01}_{1.3E-1}$	$2.3E-{01}_{1.0E-1}$	$4.4E-{01}_{8.5E-2}$	$4.5E-{01}_{1.3E-1}$	$3.2E-{01}_{6.4E-2}$	$3.8E-{01}_{9.6E-2}$
Robot-8-88-0.75-0.5	$2.8E-{01}_{8.9E-2}$	$2.8E-{01}_{9.1E-2}$	$4.0E-{01}_{7.8E-2}$	$2.2E-{01}_{9.2E-2}$	$3.6E-{01}_{8.3E-2}$	$3.7E-{01}_{1.0E-1}$	$2.8E-{01}_{1.1E-1}$	$3.7E-{01}_{9.7E-2}$
Robot-8-88-0.75-1	$5.9E-{01}_{3.0E-1}$	$5.5E-{01}_{1.9E-1}$	$8.4E-{01}_{2.7E-1}$	$5.2E-{01}_{1.9E-1}$	$7.9E-{01}_{1.6E-1}$	$8.9E-{01}_{2.5E-1}$	$5.7E-{01}_{1.9E-1}$	$7.1E-{01}_{2.9E-1}$
Robot-8-88-0.75-5	$4.1E{00}_{1.5E0}$	$4.0E{00}_{1.6E0}$	$5.7E{00}_{1.6E0}$	$5.2E{00}_{1.4E0}$	$5.8E{00}_{1.4E0}$	$5.4E{00}_{1.3E0}$	$4.9E{00}_{1.7E0}$	$5.0E{00}_{1.7E0}$
Robot-8-88-0.75-10	$1.3E{00}_{5.5E-1}$	$1.3E{00}_{6.0E-1}$	$2.1E{00}_{5.7E-1}$	$1.8E{00}_{7.1E-1}$	$1.9E{00}_{7.6E-1}$	$2.1E{00}_{4.4E-1}$	$1.4E{00}_{5.3E-1}$	$1.7E{00}_{3.6E-1}$
Robot-8-88-1-0.1	$3.8E-{01}_{1.8E-1}$	$4.5E-{01}_{1.8E-1}$	$7.4E-{01}_{2.1E-1}$	$5.1E-{01}_{2.2E-1}$	$6.4E-{01}_{1.8E-1}$	$6.9E-{01}_{1.9E-1}$	$4.8E-{01}_{2.3E-1}$	$6.6E-{01}_{2.2E-1}$
Robot-8-88-1-0.5	$5.1E-{01}_{2.5E-1}$	$5.9E-{01}_{2.2E-1}$	$8.9E-{01}_{3.4E-1}$	$6.2E-{01}_{1.6E-1}$	$8.0E-{01}_{2.7E-1}$	$9.0E-{01}_{2.8E-1}$	$5.5E-{01}_{2.2E-1}$	$6.5E-{01}_{2.2E-1}$
Robot-8-88-1-1	$5.1E-{01}_{2.3E-1}$	$5.7E-{01}_{2.2E-1}$	$9.0E-{01}_{2.5E-1}$	$5.3E-{01}_{1.9E-1}$	$6.5E-{01}_{2.0E-1}$	$7.5E-{01}_{2.2E-1}$	$5.1E-{01}_{2.2E-1}$	$6.1E-{01}_{2.0E-1}$
Robot-8-88-1-5	$1.4E{00}_{5.5E-1}$	$1.4E{00}_{5.7E-1}$	$1.7E{00}_{6.6E-1}$	$1.5E{00}_{6.6E-1}$	$1.8E{00}_{5.5E-1}$	$1.8E{00}_{6.2E-1}$	$1.4E{00}_{6.9E-1}$	$1.7E{00}_{6.6E-1}$
Robot-8-88-1-10	$3.6E{00}_{1.2E0}$	$3.5E{00}_{1.3E0}$	$5.6E{00}_{1.4E0}$	$5.0E{00}_{1.5E0}$	$4.9E{00}_{1.3E0}$	$4.7E{00}_{1.3E0}$	$3.8E{00}_{1.2E0}$	$4.8E{00}_{1.0E0}$
Robot-16-88-0.1-0.1	$6.2E-{01}_{1.7E-1}$	$6.6E-{01}_{1.6E-1}$	$1.3E{00}_{2.6E-1}$	$3.2E-{01}_{1.6E-1}$	$1.0E{00}_{2.1E-1}$	$1.3E{00}_{2.0E-1}$	$6.2E-{01}_{1.8E-1}$	$9.6E-{01}_{1.7E-1}$
Robot-16-88-0.1-0.5	$5.9E-{01}_{1.8E-1}$	$6.1E-{01}_{1.9E-1}$	$1.1E{00}_{2.3E-1}$	$4.6E-{01}_{1.8E-1}$	$9.5E-{01}_{2.5E-1}$	$9.6E-{01}_{2.0E-1}$	$6.6E-{01}_{1.7E-1}$	$8.3E-{01}_{1.6E-1}$
Robot-16-88-0.1-1	$5.5E-{01}_{1.8E-1}$	$5.9E-{01}_{2.2E-1}$	$1.0E{00}_{2.7E-1}$	$4.1E-{01}_{1.9E-1}$	$8.3E-{01}_{2.0E-1}$	$9.7E-{01}_{2.2E-1}$	$6.5E-{01}_{2.3E-1}$	$7.5E-{01}_{2.3E-1}$
Robot-16-88-0.1-5	$8.7E-{01}_{2.8E-1}$	$8.4E-{01}_{2.8E-1}$	$1.2E{00}_{3.5E-1}$	$4.8E-{01}_{2.5E-1}$	$1.2E{00}_{3.7E-1}$	$1.3E{00}_{2.6E-1}$	$8.1E-{01}_{3.7E-1}$	$1.0E{00}_{2.3E-1}$
Robot-16-88-0.1-10	$1.2E{00}_{3.4E-1}$	$1.1E{00}_{2.9E-1}$	$1.7E{00}_{3.5E-1}$	$7.1E-{01}_{3.7E-1}$	$1.6E{00}_{3.5E-1}$	$1.6E{00}_{3.7E-1}$	$1.1E{00}_{3.3E-1}$	$1.5E{00}_{3.7E-1}$
Robot-16-88-0.25-0.1	$5.3E-{01}_{1.8E-1}$	$4.5E-{01}_{1.6E-1}$	$1.0E{00}_{2.9E-1}$	$2.8E-{01}_{1.4E-1}$	$9.0E-{01}_{2.6E-1}$	$1.1E{00}_{2.7E-1}$	$4.7E-{01}_{1.6E-1}$	$7.3E-{01}_{1.9E-1}$
Robot-16-88-0.25-0.5	$9.9E-{01}_{3.2E-1}$	$9.4E-{01}_{2.6E-1}$	$1.6E{00}_{4.1E-1}$	$7.0E-{01}_{2.8E-1}$	$1.4E{00}_{2.6E-1}$	$1.4E{00}_{2.6E-1}$	$9.6E-{01}_{2.6E-1}$	$1.2E{00}_{2.8E-1}$
Robot-16-88-0.25-1	$4.2E-{01}_{1.1E-1}$	$4.5E-{01}_{1.0E-1}$	$6.9E-{01}_{1.4E-1}$	$3.0E-{01}_{1.5E-1}$	$6.1E-{01}_{1.1E-1}$	$5.8E-{01}_{1.7E-1}$	$4.2E-{01}_{1.1E-1}$	$5.2E-{01}_{1.0E-1}$
Robot-16-88-0.25-5	$7.8E-{01}_{2.9E-1}$	$8.6E-{01}_{2.3E-1}$	$9.0E-{01}_{2.2E-1}$	$6.1E-{01}_{2.6E-1}$	$9.8E-{01}_{2.5E-1}$	$9.9E-{01}_{2.8E-1}$	$8.3E-{01}_{2.2E-1}$	$1.0E{00}_{2.5E-1}$
Robot-16-88-0.5-0.1	$5.8E-{01}_{1.9E-1}$	$5.2E-{01}_{1.8E-1}$	$9.9E-{01}_{2.3E-1}$	$2.7E-{01}_{1.5E-1}$	$9.1E-{01}_{2.3E-1}$	$1.1E{00}_{2.4E-1}$	$5.1E-{01}_{2.4E-1}$	$7.4E-{01}_{2.6E-1}$
Robot-16-88-0.5-0.5	$1.4E{00}_{6.3E-1}$	$1.3E{00}_{4.5E-1}$	$2.2E{00}_{6.8E-1}$	$1.1E{00}_{5.3E-1}$	$1.8E{00}_{5.8E-1}$	$2.3E{00}_{7.2E-1}$	$1.3E{00}_{6.5E-1}$	$1.4E{00}_{5.2E-1}$
Robot-16-88-0.5-1	$2.4E-{01}_{8.3E-2}$	$3.0E-{01}_{8.8E-2}$	$4.4E-{01}_{1.2E-1}$	$2.2E-{01}_{7.7E-2}$	$4.1E-{01}_{1.2E-1}$	$4.1E-{01}_{1.1E-1}$	$2.6E-{01}_{9.4E-2}$	$3.5E-{01}_{1.1E-1}$
Robot-16-88-0.5-5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Robot-16-88-0.5-10	$1.3E{01}_{6.7E0}$	$1.6E{01}_{7.7E0}$	$2.2E{01}_{6.6E0}$	$1.0E{01}_{6.5E0}$	$2.1E{01}_{8.9E0}$	$2.0E{01}_{5.6E0}$	$1.4E{01}_{6.7E0}$	$1.7E{01}_{6.6E0}$
Robot-16-88-0.75-0.1	$1.7E-{01}_{4.8E-2}$	$1.7E-{01}_{7.1E-2}$	$2.8E-{01}_{7.4E-2}$	$9.4E-{02}_{4.0E-2}$	$2.5E-{01}_{5.8E-2}$	$2.5E-{01}_{7.5E-2}$	$1.6E-{01}_{5.0E-2}$	$2.2E-{01}_{5.0E-2}$
Robot-16-88-0.75-0.5	$4.6E-{01}_{1.4E-1}$	$4.7E-{01}_{1.9E-1}$	$7.2E-{01}_{1.7E-1}$	$2.5E-{01}_{1.1E-1}$	$6.7E-{01}_{1.3E-1}$	$6.6E-{01}_{1.9E-1}$	$4.7E-{01}_{1.6E-1}$	$6.3E-{01}_{1.7E-1}$
Robot-16-88-0.75-1	$2.9E-{01}_{8.8E-2}$	$2.9E-{01}_{1.1E-1}$	$4.2E-{01}_{1.1E-1}$	$1.8E-{01}_{6.6E-2}$	$3.8E-{01}_{8.8E-2}$	$3.9E-{01}_{1.0E-1}$	$2.9E-{01}_{9.3E-2}$	$3.3E-{01}_{1.0E-1}$
Robot-16-88-0.75-5	$7.4E{00}_{1.9E0}$	$7.3E{00}_{2.4E0}$	$1.0E{01}_{2.5E0}$	$6.2E{00}_{2.7E0}$	$9.2E{00}_{2.5E0}$	$1.0E{01}_{2.8E0}$	$7.5E{00}_{2.1E0}$	$8.7E{00}_{1.9E0}$
Robot-16-88-0.75-10	$1.7E{00}_{5.0E-1}$	$1.8E{00}_{5.6E-1}$	$2.6E{00}_{5.6E-1}$	$9.8E-{01}_{4.3E-1}$	$2.5E{00}_{5.9E-1}$	$2.4E{00}_{4.7E-1}$	$1.7E{00}_{4.7E-1}$	$2.1E{00}_{5.3E-1}$
Robot-16-88-1-0.1	$5.2E-{01}_{1.5E-1}$	$5.6E-{01}_{2.0E-1}$	$8.8E-{01}_{2.3E-1}$	$4.0E-{01}_{1.5E-1}$	$8.5E-{01}_{2.1E-1}$	$8.2E-{01}_{2.1E-1}$	$5.8E-{01}_{1.7E-1}$	$7.0E-{01}_{1.7E-1}$
Robot-16-88-1-0.5	$1.4E{00}_{6.4E-1}$	$1.4E{00}_{5.8E-1}$	$2.8E{00}_{8.3E-1}$	$1.3E{00}_{7.5E-1}$	$1.9E{00}_{6.5E-1}$	$2.4E{00}_{8.1E-1}$	$1.4E{00}_{5.7E-1}$	$1.7E{00}_{6.6E-1}$
Robot-16-88-1-1	$2.4E-{01}_{1.0E-1}$	$2.7E-{01}_{9.5E-2}$	$3.9E-{01}_{1.2E-1}$	$2.1E-{01}_{1.2E-1}$	$3.2E-{01}_{8.1E-2}$	$4.0E-{01}_{1.1E-1}$	$2.7E-{01}_{1.1E-1}$	$3.2E-{01}_{1.1E-1}$
Robot-16-88-1-5	$1.2E{00}_{4.2E-1}$	$1.3E{00}_{3.9E-1}$	$1.7E{00}_{3.8E-1}$	$8.8E-{01}_{3.8E-1}$	$1.7E{00}_{4.4E-1}$	$1.8E{00}_{4.8E-1}$	$1.1E{00}_{3.4E-1}$	$1.6E{00}_{4.4E-1}$
Robot-16-88-1-10	$7.3E-{01}_{2.0E-1}$	$7.3E-{01}_{2.7E-1}$	$1.0E{00}_{3.1E-1}$	$5.8E-{01}_{3.0E-1}$	$1.0E{00}_{2.8E-1}$	$1.1E{00}_{3.1E-1}$	$7.1E-{01}_{2.9E-1}$	$9.7E-{01}_{3.5E-1}$
Robot-32-88-0.1-0.1	$2.6E-{01}_{6.7E-2}$	$2.5E-{01}_{8.2E-2}$	$3.6E-{01}_{7.7E-2}$	$1.3E-{01}_{6.5E-2}$	$3.4E-{01}_{6.0E-2}$	$3.1E-{01}_{4.6E-2}$	$2.1E-{01}_{5.4E-2}$	$2.9E-{01}_{8.1E-2}$
Robot-32-88-0.1-0.5	$1.1E{00}_{3.9E-1}$	$1.1E{00}_{3.5E-1}$	$1.4E{00}_{3.7E-1}$	$6.5E-{01}_{3.8E-1}$	$1.5E{00}_{3.3E-1}$	$1.2E{00}_{3.6E-1}$	$9.4E-{01}_{2.6E-1}$	$1.3E{00}_{2.7E-1}$
Robot-32-88-0.1-1	$7.1E-{01}_{3.3E-1}$	$6.2E-{01}_{3.1E-1}$	$1.5E{00}_{3.4E-1}$	$5.4E-{01}_{3.1E-1}$	$1.2E{00}_{3.5E-1}$	$1.9E{00}_{4.2E-1}$	$7.3E-{01}_{2.7E-1}$	$1.0E{00}_{3.2E-1}$
Robot-32-88-0.1-5	$6.9E-{01}_{2.6E-1}$	$7.5E-{01}_{2.6E-1}$	$1.1E{00}_{2.6E-1}$	$4.6E-{01}_{2.1E-1}$	$1.0E{00}_{1.9E-1}$	$1.1E{00}_{3.1E-1}$	$8.0E-{01}_{2.2E-1}$	$9.6E-{01}_{2.4E-1}$
Robot-32-88-0.1-10	$1.4E{00}_{4.8E-1}$	$1.5E{00}_{4.7E-1}$	$2.2E{00}_{5.2E-1}$	$7.8E-{01}_{4.4E-1}$	$2.1E{00}_{4.5E-1}$	$2.1E{00}_{4.8E-1}$	$1.5E{00}_{5.2E-1}$	$1.8E{00}_{4.2E-1}$
Robot-32-88-0.25-0.1	$2.1E-{01}_{5.6E-2}$	$2.1E-{01}_{4.6E-2}$	$3.5E-{01}_{6.8E-2}$	$1.4E-{01}_{5.2E-2}$	$3.3E-{01}_{6.8E-2}$	$4.0E-{01}_{6.0E-2}$	$2.1E-{01}_{3.6E-2}$	$2.7E-{01}_{5.0E-2}$
Robot-32-88-0.25-0.5	$6.9E-{01}_{1.5E-1}$	$6.4E-{01}_{1.2E-1}$	$1.1E{00}_{1.9E-1}$	$4.2E-{01}_{1.5E-1}$	$9.6E-{01}_{2.1E-1}$	$1.1E{00}_{2.4E-1}$	$6.6E-{01}_{1.8E-1}$	$8.0E-{01}_{1.9E-1}$
Robot-32-88-0.25-1	$2.6E{00}_{8.2E-1}$	$2.5E{00}_{8.6E-1}$	$3.7E{00}_{1.0E0}$	$1.5E{00}_{8.2E-1}$	$3.4E{00}_{9.7E-1}$	$2.6E{00}_{1.0E0}$	$2.5E{00}_{8.0E-1}$	$3.4E{00}_{9.1E-1}$
Robot-32-88-0.25-5	$8.9E-{01}_{2.5E-1}$	$8.9E-{01}_{3.8E-1}$	$1.4E{00}_{3.2E-1}$	$7.0E-{01}_{3.3E-1}$	$1.3E{00}_{3.4E-1}$	$1.6E{00}_{6.2E-1}$	$9.6E-{01}_{3.2E-1}$	$1.2E{00}_{2.9E-1}$
Robot-32-88-0.25-10	$9.8E-{01}_{4.1E-1}$	$8.8E-{01}_{3.3E-1}$	$1.4E{00}_{3.7E-1}$	$4.8E-{01}_{2.5E-1}$	$1.4E{00}_{2.9E-1}$	$1.2E{00}_{3.3E-1}$	$8.0E-{01}_{2.5E-1}$	$1.2E{00}_{2.7E-1}$
Robot-32-88-0.5-0.1	$3.0E-{01}_{7.2E-2}$	$2.8E-{01}_{7.6E-2}$	$5.1E-{01}_{1.2E-1}$	$1.8E-{01}_{8.1E-2}$	$4.7E-{01}_{7.5E-2}$	$6.1E-{01}_{1.1E-1}$	$3.3E-{01}_{8.3E-2}$	$3.8E-{01}_{6.4E-2}$
Robot-32-88-0.5-0.5	$2.2E-{01}_{5.3E-2}$	$2.5E-{01}_{7.8E-2}$	$4.2E-{01}_{9.8E-2}$	$1.8E-{01}_{7.6E-2}$	$3.7E-{01}_{7.7E-2}$	$5.4E-{01}_{1.1E-1}$	$2.4E-{01}_{7.2E-2}$	$2.7E-{01}_{7.9E-2}$
Robot-32-88-0.5-1	$1.9E{00}_{9.0E-1}$	$1.8E{00}_{8.4E-1}$	$3.1E{00}_{9.7E-1}$	$1.8E{00}_{9.7E-1}$	$2.7E{00}_{7.4E-1}$	$4.3E{00}_{1.3E0}$	$2.0E{00}_{8.0E-1}$	$2.3E{00}_{6.7E-1}$
Robot-32-88-0.5-5	$4.6E-{01}_{1.3E-1}$	$4.2E-{01}_{1.5E-1}$	$7.7E-{01}_{1.7E-1}$	$3.2E-{01}_{1.5E-1}$	$7.6E-{01}_{1.2E-1}$	$7.9E-{01}_{1.7E-1}$	$5.0E-{01}_{1.9E-1}$	$5.8E-{01}_{1.2E-1}$
Robot-32-88-0.5-10	$7.8E-{01}_{2.4E-1}$	$6.6E-{01}_{2.5E-1}$	$9.6E-{01}_{2.5E-1}$	$5.3E-{01}_{3.4E-1}$	$1.0E{00}_{2.9E-1}$	$1.0E{00}_{2.7E-1}$	$7.7E-{01}_{3.3E-1}$	$8.2E-{01}_{2.9E-1}$
Robot-32-88-0.75-0.1	$2.0E-{01}_{6.5E-2}$	$2.3E-{01}_{6.3E-2}$	$3.4E-{01}_{7.8E-2}$	$1.3E-{01}_{6.5E-2}$	$3.2E-{01}_{6.9E-2}$	$2.9E-{01}_{7.8E-2}$	$2.2E-{01}_{7.2E-2}$	$2.8E-{01}_{6.6E-2}$
Robot-32-88-0.75-0.5	$4.7E-{01}_{8.6E-2}$	$4.8E-{01}_{1.2E-1}$	$7.0E-{01}_{1.6E-1}$	$3.2E-{01}_{1.1E-1}$	$5.8E-{01}_{1.0E-1}$	$7.0E-{01}_{1.4E-1}$	$4.4E-{01}_{1.3E-1}$	$5.8E-{01}_{1.1E-1}$
Robot-32-88-0.75-1	$2.8E-{01}_{1.1E-1}$	$3.1E-{01}_{1.3E-1}$	$5.0E-{01}_{1.6E-1}$	$2.2E-{01}_{1.1E-1}$	$4.5E-{01}_{1.6E-1}$	$5.7E-{01}_{1.7E-1}$	$3.1E-{01}_{1.1E-1}$	$3.9E-{01}_{1.1E-1}$
Robot-32-88-0.75-5	$1.3E{00}_{4.5E-1}$	$1.3E{00}_{4.9E-1}$	$1.7E{00}_{4.3E-1}$	$9.0E-{01}_{3.8E-1}$	$1.8E{00}_{4.7E-1}$	$1.6E{00}_{4.3E-1}$	$1.2E{00}_{3.7E-1}$	$1.5E{00}_{5.0E-1}$
Robot-32-88-0.75-10	$8.4E-{01}_{3.9E-1}$	$8.3E-{01}_{3.3E-1}$	$1.0E{00}_{3.8E-1}$	$3.3E-{01}_{2.1E-1}$	$1.1E{00}_{3.0E-1}$	$1.2E{00}_{3.5E-1}$	$8.5E-{01}_{3.2E-1}$	$8.9E-{01}_{2.7E-1}$
Robot-32-88-1-0.1	$1.6E-{01}_{5.2E-2}$	$1.5E-{01}_{6.2E-2}$	$2.9E-{01}_{6.3E-2}$	$1.1E-{01}_{5.4E-2}$	$2.5E-{01}_{6.0E-2}$	$2.6E-{01}_{9.5E-2}$	$1.5E-{01}_{6.0E-2}$	$1.9E-{01}_{5.0E-2}$
Robot-32-88-1-0.5	$3.7E-{01}_{1.3E-1}$	$3.3E-{01}_{1.1E-1}$	$5.2E-{01}_{1.4E-1}$	$2.8E-{01}_{1.0E-1}$	$5.1E-{01}_{1.4E-1}$	$6.2E-{01}_{1.8E-1}$	$3.3E-{01}_{1.2E-1}$	$4.3E-{01}_{1.4E-1}$
Robot-32-88-1-1	$4.2E-{01}_{1.5E-1}$	$4.5E-{01}_{1.3E-1}$	$8.1E-{01}_{2.0E-1}$	$3.2E-{01}_{1.7E-1}$	$6.8E-{01}_{1.7E-1}$	$7.3E-{01}_{2.0E-1}$	$5.5E-{01}_{1.5E-1}$	$5.8E-{01}_{1.7E-1}$
Robot-32-88-1-5	$6.6E-{01}_{1.8E-1}$	$7.4E-{01}_{2.1E-1}$	$1.2E{00}_{3.9E-1}$	$4.1E-{01}_{2.1E-1}$	$1.0E{00}_{2.6E-1}$	$1.1E{00}_{2.3E-1}$	$7.5E-{01}_{2.5E-1}$	$9.8E-{01}_{2.6E-1}$
Robot-32-88-1-10	$1.4E{02}_{4.4E1}$	$1.6E{02}_{4.9E1}$	$2.2E{02}_{5.3E1}$	$1.2E{02}_{3.8E1}$	$2.0E{02}_{4.4E1}$	$2.4E{02}_{5.8E1}$	$1.6E{02}_{4.3E1}$	$1.7E{02}_{6.2E1}$
Robot-64-88-0.1-0.1	$8.0E-{02}_{2.4E-2}$	$6.3E-{02}_{2.3E-2}$	$1.1E-{01}_{2.1E-2}$	$4.5E-{02}_{1.9E-2}$	$1.1E-{01}_{2.3E-2}$	$1.2E-{01}_{2.2E-2}$	$6.4E-{02}_{2.3E-2}$	$9.6E-{02}_{2.1E-2}$
Robot-64-88-0.1-0.5	$4.7E-{01}_{2.3E-1}$	$4.3E-{01}_{1.8E-1}$	$7.8E-{01}_{3.2E-1}$	$3.7E-{01}_{2.1E-1}$	$8.2E-{01}_{2.6E-1}$	$1.7E{00}_{3.3E-1}$	$5.5E-{01}_{1.6E-1}$	$5.8E-{01}_{2.4E-1}$
Robot-64-88-0.1-1	$1.7E{01}_{7.0E0}$	$1.7E{01}_{5.8E0}$	$2.5E{01}_{6.7E0}$	$1.5E{01}_{6.6E0}$	$2.3E{01}_{6.7E0}$	$4.5E{01}_{8.7E0}$	$1.8E{01}_{6.4E0}$	$1.8E{01}_{5.6E0}$
Robot-64-88-0.1-5	$2.2E-{01}_{5.4E-2}$	$2.4E-{01}_{7.1E-2}$	$3.8E-{01}_{8.6E-2}$	$1.6E-{01}_{6.7E-2}$	$3.5E-{01}_{8.1E-2}$	$4.3E-{01}_{9.8E-2}$	$2.3E-{01}_{7.5E-2}$	$2.7E-{01}_{7.0E-2}$
Robot-64-88-0.1-10	$2.3E{00}_{8.3E-1}$	$2.4E{00}_{8.0E-1}$	$2.8E{00}_{9.8E-1}$	$1.4E{00}_{7.1E-1}$	$2.9E{00}_{8.6E-1}$	$4.3E{00}_{1.0E0}$	$2.3E{00}_{8.0E-1}$	$2.7E{00}_{8.6E-1}$
Robot-64-88-0.25-0.1	$7.4E-{02}_{2.3E-2}$	$7.3E-{02}_{2.2E-2}$	$1.1E-{01}_{2.7E-2}$	$4.9E-{02}_{1.9E-2}$	$1.2E-{01}_{1.7E-2}$	$1.4E-{01}_{2.9E-2}$	$6.8E-{02}_{1.6E-2}$	$9.1E-{02}_{2.2E-2}$
Robot-64-88-0.25-0.5	$6.5E-{02}_{2.0E-2}$	$6.9E-{02}_{3.0E-2}$	$1.0E-{01}_{3.0E-2}$	$4.8E-{02}_{1.4E-2}$	$9.9E-{02}_{3.2E-2}$	$1.1E-{01}_{1.9E-2}$	$7.0E-{02}_{2.0E-2}$	$8.6E-{02}_{1.8E-2}$
Robot-64-88-0.25-1	$9.1E-{02}_{3.2E-2}$	$9.6E-{02}_{2.9E-2}$	$1.5E-{01}_{3.1E-2}$	$5.8E-{02}_{2.5E-2}$	$1.5E-{01}_{3.8E-2}$	$1.9E-{01}_{4.3E-2}$	$9.9E-{02}_{3.0E-2}$	$1.3E-{01}_{3.3E-2}$
Robot-64-88-0.25-5	$9.3E-{01}_{3.3E-1}$	$8.5E-{01}_{3.9E-1}$	$1.6E{00}_{4.6E-1}$	$7.1E-{01}_{2.6E-1}$	$1.6E{00}_{4.7E-1}$	$1.6E{00}_{4.3E-1}$	$8.9E-{01}_{3.2E-1}$	$1.2E{00}_{3.3E-1}$
Robot-64-88-0.25-10	$1.7E{00}_{5.4E-1}$	$1.7E{00}_{5.1E-1}$	$2.7E{00}_{5.5E-1}$	$1.2E{00}_{6.0E-1}$	$2.5E{00}_{5.3E-1}$	$2.9E{00}_{7.0E-1}$	$2.0E{00}_{5.7E-1}$	$2.0E{00}_{5.1E-1}$
Robot-64-88-0.5-0.1	$1.1E-{01}_{2.8E-2}$	$1.0E-{01}_{3.5E-2}$	$1.3E-{01}_{3.4E-2}$	$5.8E-{02}_{2.4E-2}$	$1.3E-{01}_{3.5E-2}$	$1.2E-{01}_{2.8E-2}$	$8.9E-{02}_{3.6E-2}$	$1.2E-{01}_{3.2E-2}$
Robot-64-88-0.5-0.5	$4.0E-{01}_{1.6E-1}$	$4.4E-{01}_{1.5E-1}$	$7.8E-{01}_{1.9E-1}$	$4.0E-{01}_{1.6E-1}$	$7.8E-{01}_{1.7E-1}$	$1.5E{00}_{3.0E-1}$	$4.7E-{01}_{1.6E-1}$	$4.8E-{01}_{1.6E-1}$
Robot-64-88-0.5-1	$1.9E-{01}_{7.4E-2}$	$1.8E-{01}_{6.5E-2}$	$3.0E-{01}_{7.6E-2}$	$1.4E-{01}_{6.1E-2}$	$3.1E-{01}_{8.5E-2}$	$4.9E-{01}_{1.4E-1}$	$2.1E-{01}_{5.6E-2}$	$2.1E-{01}_{8.2E-2}$
Robot-64-88-0.5-5	$8.5E-{01}_{3.7E-1}$	$9.0E-{01}_{2.9E-1}$	$1.3E{00}_{2.8E-1}$	$4.4E-{01}_{2.5E-1}$	$1.4E{00}_{4.1E-1}$	$1.2E{00}_{2.8E-1}$	$9.1E-{01}_{3.2E-1}$	$1.1E{00}_{2.5E-1}$
Robot-64-88-0.5-10	$2.7E{00}_{1.1E0}$	$2.7E{00}_{1.0E0}$	$3.8E{00}_{7.4E-1}$	$1.9E{00}_{9.2E-1}$	$4.3E{00}_{8.1E-1}$	$4.0E{00}_{1.0E0}$	$2.7E{00}_{9.1E-1}$	$3.8E{00}_{8.4E-1}$
Robot-64-88-0.75-0.1	$9.0E-{02}_{2.5E-2}$	$8.3E-{02}_{2.1E-2}$	$1.4E-{01}_{3.1E-2}$	$5.1E-{02}_{2.2E-2}$	$1.4E-{01}_{2.4E-2}$	$1.6E-{01}_{2.6E-2}$	$8.1E-{02}_{3.1E-2}$	$1.2E-{01}_{3.1E-2}$
Robot-64-88-0.75-0.5	$1.3E-{01}_{4.3E-2}$	$1.2E-{01}_{4.7E-2}$	$2.1E-{01}_{5.3E-2}$	$9.8E-{02}_{4.2E-2}$	$1.9E-{01}_{3.3E-2}$	$2.3E-{01}_{5.4E-2}$	$1.3E-{01}_{4.7E-2}$	$1.6E-{01}_{4.8E-2}$
Robot-64-88-0.75-1	$7.5E-{01}_{2.9E-1}$	$8.3E-{01}_{2.8E-1}$	$1.0E{00}_{3.0E-1}$	$5.7E-{01}_{3.0E-1}$	$1.1E{00}_{2.9E-1}$	$7.1E-{01}_{1.7E-1}$	$7.2E-{01}_{3.7E-1}$	$1.0E{00}_{3.6E-1}$
Robot-64-88-0.75-5	$2.1E-{01}_{6.6E-2}$	$1.9E-{01}_{5.6E-2}$	$2.7E-{01}_{5.3E-2}$	$1.2E-{01}_{5.4E-2}$	$2.8E-{01}_{6.5E-2}$	$3.1E-{01}_{9.8E-2}$	$1.8E-{01}_{7.9E-2}$	$2.1E-{01}_{6.7E-2}$
Robot-64-88-0.75-10	$5.3E-{01}_{1.5E-1}$	$5.9E-{01}_{2.2E-1}$	$8.6E-{01}_{1.7E-1}$	$4.0E-{01}_{1.6E-1}$	$8.6E-{01}_{2.0E-1}$	$8.7E-{01}_{2.2E-1}$	$5.6E-{01}_{1.9E-1}$	$7.6E-{01}_{1.5E-1}$
Robot-64-88-1-0.1	$2.7E-{01}_{7.0E-2}$	$2.9E-{01}_{8.6E-2}$	$4.4E-{01}_{7.5E-2}$	$2.0E-{01}_{7.6E-2}$	$4.0E-{01}_{7.5E-2}$	$4.3E-{01}_{9.6E-2}$	$2.7E-{01}_{6.3E-2}$	$3.8E-{01}_{8.3E-2}$
Robot-64-88-1-0.5	$2.0E-{01}_{1.0E-1}$	$1.9E-{01}_{7.0E-2}$	$4.0E-{01}_{9.3E-2}$	$2.0E-{01}_{9.3E-2}$	$3.6E-{01}_{9.7E-2}$	$5.6E-{01}_{1.3E-1}$	$2.3E-{01}_{9.8E-2}$	$2.7E-{01}_{7.9E-2}$
Robot-64-88-1-1	$4.1E-{01}_{1.2E-1}$	$3.9E-{01}_{1.0E-1}$	$5.8E-{01}_{1.1E-1}$	$3.2E-{01}_{1.1E-1}$	$5.9E-{01}_{1.2E-1}$	$6.1E-{01}_{1.1E-1}$	$3.8E-{01}_{1.1E-1}$	$5.0E-{01}_{1.3E-1}$
Robot-64-88-1-5	$2.2E-{01}_{7.7E-2}$	$2.5E-{01}_{6.6E-2}$	$3.2E-{01}_{6.3E-2}$	$1.5E-{01}_{6.4E-2}$	$3.7E-{01}_{8.5E-2}$	$3.4E-{01}_{8.5E-2}$	$2.3E-{01}_{7.0E-2}$	$2.9E-{01}_{8.2E-2}$
Robot-64-88-1-10	$4.5E-{01}_{1.6E-1}$	$4.8E-{01}_{1.5E-1}$	$6.6E-{01}_{1.2E-1}$	$3.5E-{01}_{1.1E-1}$	$7.1E-{01}_{1.7E-1}$	$7.6E-{01}_{1.8E-1}$	$4.4E-{01}_{1.3E-1}$	$5.7E-{01}_{1.3E-1}$

and

Table 10. IGD+ mean and standard deviation of the eight algorithms over 30 independent runs. Dark/light gray emphasize the best/second-best results.

Problem	AGEMOEA	AGEMOEA2	GWASFGA	MOCell	MOMBI	MOMBI2	NSGA2	SMS-EMOA
Sparse-8-96-0.1-0.1	$1.7E-{01}_{5.1E-2}$	$1.8E-{01}_{5.4E-2}$	$2.8E-{01}_{4.6E-2}$	$1.5E-{01}_{7.2E-2}$	$2.4E-{01}_{4.1E-2}$	$2.5E-{01}_{5.9E-2}$	$1.6E-{01}_{4.1E-2}$	$2.2E-{01}_{4.9E-2}$
Sparse-8-96-0.1-0.5	$1.1E-{01}_{3.5E-2}$	$1.1E-{01}_{4.0E-2}$	$2.1E-{01}_{4.3E-2}$	$1.1E-{01}_{4.0E-2}$	$1.7E-{01}_{4.3E-2}$	$1.8E-{01}_{4.7E-2}$	$1.2E-{01}_{4.0E-2}$	$1.7E-{01}_{4.6E-2}$
Sparse-8-96-0.1-1	$1.4E-{01}_{3.7E-2}$	$1.4E-{01}_{4.5E-2}$	$2.0E-{01}_{3.9E-2}$	$1.1E-{01}_{3.8E-2}$	$1.8E-{01}_{4.6E-2}$	$1.8E-{01}_{4.1E-2}$	$1.4E-{01}_{4.4E-2}$	$1.8E-{01}_{4.2E-2}$
Sparse-8-96-0.1-5	$2.3E-{01}_{7.4E-2}$	$2.1E-{01}_{7.1E-2}$	$3.7E-{01}_{7.6E-2}$	$1.8E-{01}_{7.7E-2}$	$3.3E-{01}_{8.8E-2}$	$3.6E-{01}_{6.9E-2}$	$2.5E-{01}_{9.5E-2}$	$3.1E-{01}_{9.9E-2}$
Sparse-8-96-0.1-10	$2.8E-{01}_{1.2E-1}$	$2.8E-{01}_{9.5E-2}$	$4.2E-{01}_{1.0E-1}$	$2.0E-{01}_{1.0E-1}$	$4.2E-{01}_{9.3E-2}$	$4.3E-{01}_{7.1E-2}$	$3.0E-{01}_{1.1E-1}$	$4.0E-{01}_{1.0E-1}$
Sparse-8-96-0.25-0.1	$1.4E-{01}_{2.8E-2}$	$1.4E-{01}_{4.1E-2}$	$1.9E-{01}_{3.9E-2}$	$1.1E-{01}_{4.3E-2}$	$1.9E-{01}_{3.3E-2}$	$2.0E-{01}_{4.4E-2}$	$1.3E-{01}_{3.1E-2}$	$1.9E-{01}_{3.5E-2}$
Sparse-8-96-0.25-0.5	$3.7E-{01}_{1.4E-1}$	$3.6E-{01}_{1.4E-1}$	$6.1E-{01}_{1.6E-1}$	$2.3E-{01}_{8.7E-2}$	$5.8E-{01}_{1.8E-1}$	$5.2E-{01}_{1.7E-1}$	$4.1E-{01}_{1.3E-1}$	$5.7E-{01}_{1.3E-1}$
Sparse-8-96-0.25-1	$1.3E-{01}_{3.8E-2}$	$1.3E-{01}_{5.0E-2}$	$2.0E-{01}_{3.5E-2}$	$1.0E-{01}_{2.9E-2}$	$2.1E-{01}_{5.7E-2}$	$1.9E-{01}_{4.8E-2}$	$1.3E-{01}_{4.3E-2}$	$1.9E-{01}_{4.7E-2}$
Sparse-8-96-0.25-5	$2.8E-{01}_{1.1E-1}$	$3.2E-{01}_{1.2E-1}$	$4.2E-{01}_{1.4E-1}$	$2.5E-{01}_{1.2E-1}$	$4.1E-{01}_{9.9E-2}$	$4.3E-{01}_{1.1E-1}$	$2.5E-{01}_{1.1E-1}$	$3.7E-{01}_{1.4E-1}$
Sparse-8-96-0.25-10	$7.4E-{01}_{2.0E-1}$	$6.5E-{01}_{2.4E-1}$	$8.8E-{01}_{2.7E-1}$	$6.4E-{01}_{3.0E-1}$	$9.0E-{01}_{2.7E-1}$	$7.9E-{01}_{3.1E-1}$	$6.5E-{01}_{2.5E-1}$	$7.5E-{01}_{2.3E-1}$
Sparse-8-96-0.5-0.1	$1.5E-{01}_{7.0E-2}$	$1.5E-{01}_{5.3E-2}$	$2.5E-{01}_{4.6E-2}$	$1.4E-{01}_{7.2E-2}$	$2.0E-{01}_{7.3E-2}$	$1.6E-{01}_{6.1E-2}$	$1.5E-{01}_{8.4E-2}$	$1.9E-{01}_{5.3E-2}$
Sparse-8-96-0.5-0.5	$3.9E-{01}_{1.7E-1}$	$4.6E-{01}_{1.1E-1}$	$6.9E-{01}_{1.7E-1}$	$3.9E-{01}_{1.9E-1}$	$5.9E-{01}_{1.5E-1}$	$5.6E-{01}_{1.9E-1}$	$3.9E-{01}_{1.4E-1}$	$5.1E-{01}_{1.7E-1}$
Sparse-8-96-0.5-1	$4.6E-{01}_{2.8E-1}$	$4.4E-{01}_{1.8E-1}$	$8.5E-{01}_{2.8E-1}$	$4.4E-{01}_{2.8E-1}$	$6.3E-{01}_{2.1E-1}$	$6.9E-{01}_{2.9E-1}$	$5.6E-{01}_{2.0E-1}$	$5.4E-{01}_{2.0E-1}$
Sparse-8-96-0.5-5	$2.9E-{01}_{9.7E-2}$	$2.7E-{01}_{9.6E-2}$	$4.1E-{01}_{1.0E-1}$	$2.6E-{01}_{1.1E-1}$	$4.1E-{01}_{1.3E-1}$	$4.2E-{01}_{1.3E-1}$	$2.8E-{01}_{1.2E-1}$	$3.4E-{01}_{1.0E-1}$
Sparse-8-96-0.5-10	$2.3E{00}_{9.8E-1}$	$2.5E{00}_{9.0E-1}$	$3.1E{00}_{9.4E-1}$	$2.5E{00}_{9.9E-1}$	$3.3E{00}_{8.6E-1}$	$2.9E{00}_{8.4E-1}$	$2.4E{00}_{7.9E-1}$	$2.8E{00}_{1.0E0}$
Sparse-8-96-0.75-0.1	$2.5E-{01}_{9.9E-2}$	$2.7E-{01}_{1.0E-1}$	$4.1E-{01}_{1.1E-1}$	$2.9E-{01}_{1.1E-1}$	$3.7E-{01}_{8.4E-2}$	$3.4E-{01}_{8.6E-2}$	$2.8E-{01}_{1.0E-1}$	$2.8E-{01}_{8.7E-2}$
Sparse-8-96-0.75-0.5	$3.1E-{01}_{1.2E-1}$	$3.0E-{01}_{1.2E-1}$	$4.7E-{01}_{1.2E-1}$	$3.2E-{01}_{1.3E-1}$	$4.3E-{01}_{8.5E-2}$	$4.2E-{01}_{1.0E-1}$	$2.9E-{01}_{1.3E-1}$	$4.1E-{01}_{1.0E-1}$
Sparse-8-96-0.75-1	$4.9E-{01}_{1.7E-1}$	$4.1E-{01}_{1.5E-1}$	$7.4E-{01}_{1.9E-1}$	$5.1E-{01}_{1.8E-1}$	$6.3E-{01}_{1.3E-1}$	$6.1E-{01}_{1.4E-1}$	$4.3E-{01}_{1.7E-1}$	$5.4E-{01}_{1.8E-1}$
Sparse-8-96-0.75-5	$1.7E{00}_{7.1E-1}$	$1.7E{00}_{5.8E-1}$	$2.7E{00}_{8.1E-1}$	$1.8E{00}_{6.9E-1}$	$2.4E{00}_{6.3E-1}$	$2.4E{00}_{5.2E-1}$	$1.6E{00}_{6.9E-1}$	$2.1E{00}_{7.0E-1}$
Sparse-8-96-0.75-10	$3.9E-{01}_{1.6E-1}$	$3.7E-{01}_{1.2E-1}$	$6.0E-{01}_{2.1E-1}$	$3.7E-{01}_{2.2E-1}$	$5.9E-{01}_{1.5E-1}$	$5.2E-{01}_{1.7E-1}$	$3.6E-{01}_{1.5E-1}$	$5.1E-{01}_{1.9E-1}$
Sparse-8-96-1-0.1	$5.2E-{01}_{1.8E-1}$	$6.4E-{01}_{2.1E-1}$	$8.8E-{01}_{2.1E-1}$	$7.7E-{01}_{2.6E-1}$	$8.7E-{01}_{1.7E-1}$	$8.3E-{01}_{1.9E-1}$	$6.0E-{01}_{2.1E-1}$	$7.7E-{01}_{2.0E-1}$
Sparse-8-96-1-0.5	$3.3E-{01}_{1.0E-1}$	$3.0E-{01}_{1.2E-1}$	$3.9E-{01}_{1.3E-1}$	$4.2E-{01}_{1.5E-1}$	$3.9E-{01}_{1.2E-1}$	$3.9E-{01}_{8.9E-2}$	$2.9E-{01}_{8.4E-2}$	$3.6E-{01}_{9.5E-2}$
Sparse-8-96-1-1	$3.9E-{01}_{1.5E-1}$	$3.8E-{01}_{1.1E-1}$	$5.0E-{01}_{1.4E-1}$	$4.9E-{01}_{1.2E-1}$	$5.1E-{01}_{1.3E-1}$	$5.4E-{01}_{1.2E-1}$	$3.9E-{01}_{1.1E-1}$	$4.3E-{01}_{8.4E-2}$
Sparse-8-96-1-5	$2.9E-{01}_{1.6E-1}$	$2.4E-{01}_{1.2E-1}$	$4.0E-{01}_{1.2E-1}$	$3.2E-{01}_{1.5E-1}$	$4.0E-{01}_{1.2E-1}$	$4.2E-{01}_{1.4E-1}$	$2.7E-{01}_{1.0E-1}$	$3.7E-{01}_{1.4E-1}$
Sparse-8-96-1-10	$5.3E{00}_{2.0E0}$	$6.3E{00}_{2.2E0}$	$8.6E{00}_{2.0E0}$	$5.1E{00}_{2.4E0}$	$8.6E{00}_{2.8E0}$	$8.6E{00}_{2.0E0}$	$5.0E{00}_{2.0E0}$	$7.8E{00}_{1.7E0}$
Sparse-16-96-0.1-0.1	$1.2E-{01}_{3.8E-2}$	$1.3E-{01}_{3.3E-2}$	$2.0E-{01}_{3.9E-2}$	$7.7E-{02}_{2.2E-2}$	$1.8E-{01}_{4.4E-2}$	$1.8E-{01}_{4.2E-2}$	$1.2E-{01}_{3.3E-2}$	$1.9E-{01}_{4.4E-2}$
Sparse-16-96-0.1-0.5	$2.9E-{01}_{8.6E-2}$	$2.9E-{01}_{7.2E-2}$	$4.3E-{01}_{9.8E-2}$	$1.7E-{01}_{6.4E-2}$	$4.6E-{01}_{1.1E-1}$	$4.0E-{01}_{1.1E-1}$	$3.2E-{01}_{1.2E-1}$	$4.4E-{01}_{1.0E-1}$
Sparse-16-96-0.1-1	$3.2E-{01}_{8.5E-2}$	$3.2E-{01}_{7.9E-2}$	$5.0E-{01}_{1.0E-1}$	$2.5E-{01}_{1.1E-1}$	$4.5E-{01}_{9.9E-2}$	$4.8E-{01}_{9.9E-2}$	$3.2E-{01}_{1.1E-1}$	$4.2E-{01}_{1.2E-1}$
Sparse-16-96-0.1-5	$1.5E{00}_{4.3E-1}$	$1.5E{00}_{4.7E-1}$	$2.1E{00}_{4.2E-1}$	$1.2E{00}_{4.8E-1}$	$1.9E{00}_{7.1E-1}$	$1.9E{00}_{4.3E-1}$	$1.6E{00}_{6.3E-1}$	$1.6E{00}_{4.3E-1}$
Sparse-16-96-0.1-10	$7.8E-{01}_{1.9E-1}$	$7.7E-{01}_{2.5E-1}$	$1.1E{00}_{2.2E-1}$	$5.5E-{01}_{2.4E-1}$	$1.1E{00}_{2.4E-1}$	$1.0E{00}_{2.9E-1}$	$7.9E-{01}_{2.9E-1}$	$8.6E-{01}_{2.7E-1}$
Sparse-16-96-0.25-0.1	$5.3E-{01}_{1.7E-1}$	$5.0E-{01}_{1.9E-1}$	$8.2E-{01}_{2.1E-1}$	$3.6E-{01}_{1.8E-1}$	$7.9E-{01}_{2.5E-1}$	$9.0E-{01}_{3.0E-1}$	$5.1E-{01}_{2.0E-1}$	$8.7E-{01}_{2.3E-1}$
Sparse-16-96-0.25-0.5	$1.2E{00}_{4.6E-1}$	$1.1E{00}_{5.2E-1}$	$1.6E{00}_{5.3E-1}$	$8.5E-{01}_{4.3E-1}$	$1.8E{00}_{4.0E-1}$	$1.7E{00}_{4.8E-1}$	$1.1E{00}_{4.2E-1}$	$1.8E{00}_{4.5E-1}$
Sparse-16-96-0.25-1	$3.3E-{01}_{1.2E-1}$	$3.2E-{01}_{1.0E-1}$	$5.0E-{01}_{1.1E-1}$	$2.3E-{01}_{1.0E-1}$	$4.4E-{01}_{1.1E-1}$	$5.0E-{01}_{1.0E-1}$	$3.2E-{01}_{9.4E-2}$	$3.9E-{01}_{1.2E-1}$
Sparse-16-96-0.25-5	$2.6E-{01}_{1.2E-1}$	$2.4E-{01}_{1.1E-1}$	$4.9E-{01}_{1.2E-1}$	$2.0E-{01}_{8.6E-2}$	$4.0E-{01}_{1.1E-1}$	$4.0E-{01}_{1.1E-1}$	$2.6E-{01}_{1.1E-1}$	$3.8E-{01}_{9.8E-2}$
Sparse-16-96-0.25-10	$4.0E-{01}_{1.8E-1}$	$3.9E-{01}_{1.9E-1}$	$5.7E-{01}_{2.2E-1}$	$2.6E-{01}_{1.6E-1}$	$5.8E-{01}_{2.1E-1}$	$6.2E-{01}_{2.1E-1}$	$3.5E-{01}_{1.3E-1}$	$5.3E-{01}_{1.8E-1}$
Sparse-16-96-0.5-0.1	$2.0E-{01}_{7.0E-2}$	$2.0E-{01}_{5.2E-2}$	$3.3E-{01}_{6.8E-2}$	$1.5E-{01}_{6.3E-2}$	$2.8E-{01}_{6.5E-2}$	$3.0E-{01}_{7.0E-2}$	$2.1E-{01}_{6.8E-2}$	$2.7E-{01}_{7.2E-2}$
Sparse-16-96-0.5-0.5	$3.3E-{01}_{8.9E-2}$	$3.7E-{01}_{1.1E-1}$	$5.2E-{01}_{8.6E-2}$	$2.6E-{01}_{1.0E-1}$	$4.7E-{01}_{8.7E-2}$	$5.1E-{01}_{8.0E-2}$	$3.5E-{01}_{7.5E-2}$	$4.8E-{01}_{9.8E-2}$
Sparse-16-96-0.5-1	$1.1E{00}_{4.6E-1}$	$1.2E{00}_{4.4E-1}$	$2.1E{00}_{5.7E-1}$	$8.0E-{01}_{5.1E-1}$	$1.6E{00}_{4.2E-1}$	$1.8E{00}_{5.3E-1}$	$9.7E-{01}_{3.5E-1}$	$1.7E{00}_{4.8E-1}$
Sparse-16-96-0.5-5	$6.5E-{01}_{3.1E-1}$	$6.5E-{01}_{3.3E-1}$	$1.0E{00}_{2.9E-1}$	$4.5E-{01}_{2.8E-1}$	$1.1E{00}_{2.2E-1}$	$9.9E-{01}_{3.1E-1}$	$7.2E-{01}_{3.1E-1}$	$7.7E-{01}_{2.8E-1}$
Sparse-16-96-0.5-10	$1.3E{00}_{6.4E-1}$	$1.5E{00}_{1.0E0}$	$2.1E{00}_{8.2E-1}$	$1.2E{00}_{1.3E0}$	$2.2E{00}_{9.9E-1}$	$2.2E{00}_{1.1E0}$	$1.8E{00}_{1.2E0}$	$1.7E{00}_{7.3E-1}$
Sparse-16-96-0.75-0.1	$8.5E-{01}_{2.3E-1}$	$8.3E-{01}_{2.4E-1}$	$1.4E{00}_{2.9E-1}$	$5.6E-{01}_{2.6E-1}$	$1.3E{00}_{3.0E-1}$	$1.3E{00}_{3.1E-1}$	$9.7E-{01}_{3.0E-1}$	$1.2E{00}_{2.4E-1}$
Sparse-16-96-0.75-0.5	$1.4E-{01}_{5.1E-2}$	$1.5E-{01}_{5.4E-2}$	$2.3E-{01}_{5.6E-2}$	$1.2E-{01}_{5.1E-2}$	$2.0E-{01}_{4.9E-2}$	$1.9E-{01}_{5.3E-2}$	$1.4E-{01}_{4.1E-2}$	$2.2E-{01}_{4.9E-2}$
Sparse-16-96-0.75-1	$3.2E-{01}_{1.2E-1}$	$3.6E-{01}_{1.2E-1}$	$5.2E-{01}_{1.1E-1}$	$2.8E-{01}_{1.1E-1}$	$4.9E-{01}_{1.3E-1}$	$5.1E-{01}_{1.9E-1}$	$3.3E-{01}_{1.1E-1}$	$4.5E-{01}_{1.2E-1}$
Sparse-16-96-0.75-5	$4.0E-{01}_{1.5E-1}$	$4.2E-{01}_{1.3E-1}$	$6.4E-{01}_{1.4E-1}$	$2.9E-{01}_{1.2E-1}$	$6.1E-{01}_{1.4E-1}$	$6.0E-{01}_{1.3E-1}$	$4.0E-{01}_{1.4E-1}$	$5.7E-{01}_{1.8E-1}$
Sparse-16-96-0.75-10	$3.9E-{01}_{1.6E-1}$	$4.5E-{01}_{1.3E-1}$	$8.5E-{01}_{2.3E-1}$	$3.7E-{01}_{1.2E-1}$	$8.6E-{01}_{2.5E-1}$	$7.8E-{01}_{2.4E-1}$	$4.7E-{01}_{1.3E-1}$	$6.4E-{01}_{2.0E-1}$
Sparse-16-96-1-0.1	$1.1E{00}_{4.1E-1}$	$1.0E{00}_{4.3E-1}$	$1.8E{00}_{4.5E-1}$	$1.1E{00}_{3.9E-1}$	$1.5E{00}_{3.4E-1}$	$1.5E{00}_{5.0E-1}$	$1.2E{00}_{5.1E-1}$	$1.5E{00}_{4.7E-1}$
Sparse-16-96-1-0.5	$3.3E{00}_{1.3E0}$	$3.2E{00}_{1.5E0}$	$4.4E{00}_{1.6E0}$	$3.2E{00}_{1.4E0}$	$4.3E{00}_{1.1E0}$	$4.1E{00}_{1.2E0}$	$3.4E{00}_{1.5E0}$	$4.1E{00}_{1.2E0}$
Sparse-16-96-1-1	$9.5E-{01}_{3.8E-1}$	$1.1E{00}_{3.6E-1}$	$1.4E{00}_{3.6E-1}$	$1.0E{00}_{2.7E-1}$	$1.3E{00}_{2.5E-1}$	$1.4E{00}_{3.4E-1}$	$1.0E{00}_{3.7E-1}$	$1.3E{00}_{2.8E-1}$
Sparse-16-96-1-5	$6.8E-{01}_{3.2E-1}$	$6.3E-{01}_{2.8E-1}$	$9.0E-{01}_{3.3E-1}$	$4.3E-{01}_{2.9E-1}$	$7.9E-{01}_{2.5E-1}$	$8.6E-{01}_{3.2E-1}$	$6.6E-{01}_{3.9E-1}$	$7.9E-{01}_{2.8E-1}$
Sparse-16-96-1-10	$5.5E-{01}_{4.2E-1}$	$5.2E-{01}_{3.5E-1}$	$5.6E-{01}_{2.9E-1}$	$6.3E-{01}_{4.4E-1}$	$5.6E-{01}_{2.4E-1}$	$6.0E-{01}_{2.9E-1}$	$5.0E-{01}_{3.5E-1}$	$4.5E-{01}_{2.0E-1}$
Sparse-32-96-0.1-0.1	$2.3E-{01}_{6.2E-2}$	$2.5E-{01}_{8.7E-2}$	$4.1E-{01}_{8.6E-2}$	$1.6E-{01}_{7.2E-2}$	$3.9E-{01}_{8.7E-2}$	$4.2E-{01}_{1.1E-1}$	$2.7E-{01}_{7.5E-2}$	$3.4E-{01}_{7.9E-2}$
Sparse-32-96-0.1-0.5	$9.5E-{01}_{2.1E-1}$	$9.8E-{01}_{2.9E-1}$	$1.4E{00}_{2.3E-1}$	$3.9E-{01}_{1.6E-1}$	$1.4E{00}_{2.0E-1}$	$1.3E{00}_{2.5E-1}$	$9.8E-{01}_{2.3E-1}$	$1.3E{00}_{2.4E-1}$
Sparse-32-96-0.1-1	$8.2E-{01}_{3.2E-1}$	$8.0E-{01}_{2.2E-1}$	$1.2E{00}_{2.6E-1}$	$3.4E-{01}_{2.1E-1}$	$1.2E{00}_{2.6E-1}$	$1.0E{00}_{2.0E-1}$	$6.8E-{01}_{2.0E-1}$	$1.2E{00}_{2.7E-1}$
Sparse-32-96-0.1-5	$1.2E{00}_{4.7E-1}$	$9.5E-{01}_{4.1E-1}$	$1.7E{00}_{5.1E-1}$	$4.9E-{01}_{3.1E-1}$	$1.8E{00}_{4.1E-1}$	$1.7E{00}_{4.7E-1}$	$1.0E{00}_{3.0E-1}$	$1.5E{00}_{3.8E-1}$
Sparse-32-96-0.1-10	$1.4E{00}_{1.0E0}$	$1.4E{00}_{7.5E-1}$	$2.1E{00}_{7.8E-1}$	$1.4E{00}_{8.7E-1}$	$2.1E{00}_{1.2E0}$	$3.0E{00}_{1.2E0}$	$1.7E{00}_{8.7E-1}$	$1.8E{00}_{6.7E-1}$
Sparse-32-96-0.25-0.1	$3.9E-{01}_{1.3E-1}$	$4.3E-{01}_{1.3E-1}$	$6.1E-{01}_{1.4E-1}$	$2.2E-{01}_{1.0E-1}$	$6.5E-{01}_{1.2E-1}$	$5.7E-{01}_{1.3E-1}$	$4.3E-{01}_{1.3E-1}$	$6.4E-{01}_{1.6E-1}$
Sparse-32-96-0.25-0.5	$4.0E-{01}_{1.2E-1}$	$4.1E-{01}_{1.4E-1}$	$5.6E-{01}_{1.3E-1}$	$3.0E-{01}_{1.4E-1}$	$5.2E-{01}_{1.3E-1}$	$5.4E-{01}_{2.0E-1}$	$4.0E-{01}_{1.5E-1}$	$4.7E-{01}_{1.6E-1}$
Sparse-32-96-0.25-1	$7.1E-{01}_{2.4E-1}$	$7.1E-{01}_{2.6E-1}$	$1.1E{00}_{2.1E-1}$	$5.7E-{01}_{2.4E-1}$	$9.9E-{01}_{2.8E-1}$	$1.1E{00}_{2.9E-1}$	$6.7E-{01}_{2.7E-1}$	$8.4E-{01}_{2.2E-1}$
Sparse-32-96-0.25-5	$1.1E{00}_{5.3E-1}$	$1.1E{00}_{5.5E-1}$	$1.5E{00}_{4.4E-1}$	$6.1E-{01}_{2.9E-1}$	$1.6E{00}_{6.3E-1}$	$2.2E{00}_{8.0E-1}$	$1.1E{00}_{5.4E-1}$	$1.3E{00}_{4.8E-1}$
Sparse-32-96-0.25-10	$4.1E{01}_{2.2E1}$	$3.4E{01}_{2.5E1}$	$4.2E{01}_{1.8E1}$	$3.4E{01}_{2.5E1}$	$5.0E{01}_{2.5E1}$	$4.9E{01}_{2.1E1}$	$4.2E{01}_{2.2E1}$	$3.2E{01}_{1.7E1}$
Sparse-32-96-0.5-0.1	$4.1E-{01}_{1.3E-1}$	$4.3E-{01}_{9.7E-2}$	$6.1E-{01}_{1.5E-1}$	$2.5E-{01}_{1.3E-1}$	$5.9E-{01}_{1.2E-1}$	$6.4E-{01}_{1.6E-1}$	$4.6E-{01}_{1.6E-1}$	$5.7E-{01}_{1.5E-1}$
Sparse-32-96-0.5-0.5	$1.8E-{01}_{4.6E-2}$	$1.6E-{01}_{5.4E-2}$	$2.7E-{01}_{6.3E-2}$	$1.2E-{01}_{5.1E-2}$	$2.7E-{01}_{5.6E-2}$	$2.6E-{01}_{6.9E-2}$	$1.7E-{01}_{5.8E-2}$	$2.4E-{01}_{5.7E-2}$
Sparse-32-96-0.5-1	$1.1E{00}_{2.7E-1}$	$1.1E{00}_{3.7E-1}$	$1.4E{00}_{4.6E-1}$	$7.5E-{01}_{3.6E-1}$	$1.4E{00}_{4.1E-1}$	$1.6E{00}_{3.0E-1}$	$1.0E{00}_{3.0E-1}$	$1.2E{00}_{3.3E-1}$
Sparse-32-96-0.5-5	$6.6E-{01}_{2.3E-1}$	$8.4E-{01}_{3.2E-1}$	$1.2E{00}_{2.3E-1}$	$5.0E-{01}_{3.0E-1}$	$1.3E{00}_{3.0E-1}$	$1.2E{00}_{3.8E-1}$	$7.0E-{01}_{2.3E-1}$	$1.0E{00}_{2.8E-1}$
Sparse-32-96-0.5-10	$1.2E{00}_{4.1E-1}$	$9.5E-{01}_{3.6E-1}$	$1.7E{00}_{2.5E-1}$	$6.4E-{01}_{3.1E-1}$	$1.9E{00}_{5.0E-1}$	$1.8E{00}_{5.1E-1}$	$1.2E{00}_{4.1E-1}$	$1.4E{00}_{3.2E-1}$
Sparse-32-96-0.75-0.1	$6.9E-{01}_{1.9E-1}$	$6.9E-{01}_{2.1E-1}$	$9.6E-{01}_{2.1E-1}$	$4.3E-{01}_{1.9E-1}$	$9.8E-{01}_{2.1E-1}$	$9.7E-{01}_{1.7E-1}$	$6.9E-{01}_{2.0E-1}$	$9.3E-{01}_{1.5E-1}$
Sparse-32-96-0.75-0.5	$3.0E-{01}_{7.7E-2}$	$2.9E-{01}_{6.7E-2}$	$4.0E-{01}_{8.1E-2}$	$2.2E-{01}_{7.9E-2}$	$3.8E-{01}_{6.9E-2}$	$3.7E-{01}_{9.0E-2}$	$2.6E-{01}_{8.8E-2}$	$3.5E-{01}_{8.7E-2}$
Sparse-32-96-0.75-1	$9.2E-{01}_{3.5E-1}$	$9.4E-{01}_{3.4E-1}$	$1.3E{00}_{3.5E-1}$	$7.3E-{01}_{3.2E-1}$	$1.3E{00}_{4.6E-1}$	$1.7E{00}_{5.3E-1}$	$1.0E{00}_{4.8E-1}$	$1.2E{00}_{3.7E-1}$
Sparse-32-96-0.75-5	$1.2E{00}_{5.1E-1}$	$1.1E{00}_{3.7E-1}$	$1.8E{00}_{5.1E-1}$	$8.7E-{01}_{4.7E-1}$	$1.7E{00}_{4.8E-1}$	$2.2E{00}_{6.0E-1}$	$1.3E{00}_{4.3E-1}$	$1.5E{00}_{4.5E-1}$
Sparse-32-96-0.75-10	$9.9E-{01}_{4.4E-1}$	$9.2E-{01}_{3.6E-1}$	$1.9E{00}_{4.6E-1}$	$4.9E-{01}_{3.3E-1}$	$1.9E{00}_{4.8E-1}$	$2.1E{00}_{4.1E-1}$	$1.0E{00}_{3.6E-1}$	$1.6E{00}_{3.4E-1}$
Sparse-32-96-1-0.1	$1.3E{00}_{4.0E-1}$	$1.2E{00}_{3.6E-1}$	$1.9E{00}_{4.5E-1}$	$6.9E-{01}_{4.5E-1}$	$1.9E{00}_{5.2E-1}$	$1.6E{00}_{4.6E-1}$	$1.1E{00}_{4.4E-1}$	$1.7E{00}_{3.7E-1}$
Sparse-32-96-1-0.5	$5.6E-{01}_{2.7E-1}$	$5.8E-{01}_{2.0E-1}$	$8.4E-{01}_{2.6E-1}$	$4.3E-{01}_{1.7E-1}$	$7.2E-{01}_{2.1E-1}$	$8.0E-{01}_{2.6E-1}$	$6.1E-{01}_{2.4E-1}$	$7.6E-{01}_{2.2E-1}$
Sparse-32-96-1-1	$3.1E-{01}_{1.3E-1}$	$3.1E-{01}_{1.3E-1}$	$4.2E-{01}_{1.2E-1}$	$2.3E-{01}_{8.6E-2}$	$4.3E-{01}_{9.3E-2}$	$4.0E-{01}_{1.2E-1}$	$3.5E-{01}_{1.5E-1}$	$3.6E-{01}_{1.0E-1}$
Sparse-32-96-1-5	$1.3E{00}_{6.3E-1}$	$1.0E{00}_{4.0E-1}$	$1.6E{00}_{4.2E-1}$	$7.1E-{01}_{3.8E-1}$	$1.7E{00}_{4.3E-1}$	$2.0E{00}_{6.4E-1}$	$1.1E{00}_{4.6E-1}$	$1.4E{00}_{5.1E-1}$
Sparse-32-96-1-10	$6.5E-{01}_{3.0E-1}$	$6.1E-{01}_{3.4E-1}$	$8.1E-{01}_{2.8E-1}$	$5.1E-{01}_{2.3E-1}$	$8.6E-{01}_{3.2E-1}$	$1.2E{00}_{4.0E-1}$	$6.3E-{01}_{3.5E-1}$	$7.7E-{01}_{2.5E-1}$
Sparse-64-96-0.1-0.1	$9.1E-{01}_{2.1E-1}$	$8.8E-{01}_{2.7E-1}$	$1.4E{00}_{2.4E-1}$	$3.6E-{01}_{1.6E-1}$	$1.6E{00}_{2.9E-1}$	$1.4E{00}_{1.9E-1}$	$8.9E-{01}_{2.3E-1}$	$1.4E{00}_{2.8E-1}$
Sparse-64-96-0.1-0.5	$9.0E-{01}_{3.5E-1}$	$1.0E{00}_{3.4E-1}$	$1.5E{00}_{3.8E-1}$	$7.7E-{01}_{3.8E-1}$	$1.5E{00}_{4.2E-1}$	$2.0E{00}_{5.9E-1}$	$8.8E-{01}_{3.5E-1}$	$1.2E{00}_{2.6E-1}$
Sparse-64-96-0.1-1	$3.5E-{01}_{1.4E-1}$	$3.8E-{01}_{1.2E-1}$	$5.8E-{01}_{1.5E-1}$	$2.5E-{01}_{1.5E-1}$	$6.3E-{01}_{1.4E-1}$	$8.3E-{01}_{1.8E-1}$	$3.9E-{01}_{1.6E-1}$	$4.5E-{01}_{1.3E-1}$
Sparse-64-96-0.1-5	$5.3E-{01}_{3.0E-1}$	$3.9E-{01}_{2.8E-1}$	$6.7E-{01}_{3.9E-1}$	$3.2E-{01}_{1.5E-1}$	$9.0E-{01}_{5.4E-1}$	$1.4E{00}_{6.9E-1}$	$4.8E-{01}_{3.0E-1}$	$4.5E-{01}_{2.5E-1}$
Sparse-64-96-0.1-10	$1.0E{00}_{5.6E-1}$	$1.0E{00}_{5.8E-1}$	$1.7E{00}_{5.5E-1}$	$8.1E-{01}_{5.4E-1}$	$2.0E{00}_{6.4E-1}$	$2.7E{00}_{6.3E-1}$	$1.2E{00}_{5.4E-1}$	$1.4E{00}_{5.6E-1}$
Sparse-64-96-0.25-0.1	$1.5E{00}_{3.7E-1}$	$1.6E{00}_{4.3E-1}$	$2.5E{00}_{4.1E-1}$	$6.1E-{01}_{3.6E-1}$	$2.7E{00}_{3.1E-1}$	$2.2E{00}_{4.2E-1}$	$1.5E{00}_{4.4E-1}$	$2.3E{00}_{3.3E-1}$
Sparse-64-96-0.25-0.5	$8.8E-{01}_{2.1E-1}$	$8.0E-{01}_{2.7E-1}$	$1.3E{00}_{2.3E-1}$	$5.5E-{01}_{2.5E-1}$	$1.2E{00}_{3.2E-1}$	$1.5E{00}_{2.7E-1}$	$7.5E-{01}_{2.9E-1}$	$1.1E{00}_{2.1E-1}$
Sparse-64-96-0.25-1	$4.9E-{01}_{1.5E-1}$	$5.3E-{01}_{1.3E-1}$	$8.0E-{01}_{1.3E-1}$	$2.3E-{01}_{1.1E-1}$	$7.8E-{01}_{1.4E-1}$	$7.6E-{01}_{2.1E-1}$	$4.9E-{01}_{1.4E-1}$	$7.2E-{01}_{1.0E-1}$
Sparse-64-96-0.25-5	$1.3E{00}_{5.5E-1}$	$1.2E{00}_{4.6E-1}$	$1.9E{00}_{8.0E-1}$	$8.1E-{01}_{4.6E-1}$	$2.8E{00}_{1.1E0}$	$3.6E{00}_{1.1E0}$	$1.6E{00}_{7.4E-1}$	$1.8E{00}_{6.0E-1}$
Sparse-64-96-0.25-10	$3.5E-{01}_{1.8E-1}$	$3.8E-{01}_{1.8E-1}$	$6.0E-{01}_{2.1E-1}$	$2.8E-{01}_{2.0E-1}$	$8.6E-{01}_{2.2E-1}$	$8.2E-{01}_{1.7E-1}$	$4.3E-{01}_{2.0E-1}$	$4.5E-{01}_{1.5E-1}$
Sparse-64-96-0.5-0.1	$4.5E-{01}_{1.4E-1}$	$4.0E-{01}_{9.9E-2}$	$7.0E-{01}_{1.4E-1}$	$2.2E-{01}_{9.3E-2}$	$7.8E-{01}_{1.3E-1}$	$6.9E-{01}_{1.1E-1}$	$4.1E-{01}_{1.1E-1}$	$6.3E-{01}_{1.4E-1}$
Sparse-64-96-0.5-0.5	$3.2E-{01}_{9.1E-2}$	$3.6E-{01}_{7.4E-2}$	$5.3E-{01}_{7.7E-2}$	$2.0E-{01}_{7.6E-2}$	$5.3E-{01}_{6.8E-2}$	$5.1E-{01}_{9.0E-2}$	$3.4E-{01}_{8.0E-2}$	$4.5E-{01}_{8.6E-2}$
Sparse-64-96-0.5-1	$2.1E-{01}_{7.4E-2}$	$2.2E-{01}_{8.9E-2}$	$3.2E-{01}_{8.7E-2}$	$1.5E-{01}_{8.8E-2}$	$3.8E-{01}_{1.0E-1}$	$4.9E-{01}_{1.9E-1}$	$2.1E-{01}_{8.3E-2}$	$2.8E-{01}_{8.9E-2}$
Sparse-64-96-0.5-5	$8.1E-{01}_{2.8E-1}$	$7.9E-{01}_{2.9E-1}$	$1.1E{00}_{2.3E-1}$	$6.9E-{01}_{3.6E-1}$	$1.3E{00}_{3.1E-1}$	$1.3E{00}_{2.6E-1}$	$7.7E-{01}_{3.3E-1}$	$9.7E-{01}_{2.3E-1}$
Sparse-64-96-0.5-10	$1.7E{00}_{9.2E-1}$	$1.9E{00}_{9.1E-1}$	$3.4E{00}_{1.0E0}$	$1.5E{00}_{9.1E-1}$	$4.6E{00}_{9.3E-1}$	$3.3E{00}_{7.5E-1}$	$1.7E{00}_{9.4E-1}$	$2.6E{00}_{7.6E-1}$
Sparse-64-96-0.75-0.1	$3.7E-{01}_{7.7E-2}$	$3.7E-{01}_{8.9E-2}$	$5.1E-{01}_{6.6E-2}$	$1.6E-{01}_{8.5E-2}$	$5.2E-{01}_{9.8E-2}$	$5.7E-{01}_{1.3E-1}$	$3.4E-{01}_{1.0E-1}$	$5.0E-{01}_{9.3E-2}$
Sparse-64-96-0.75-0.5	$6.3E-{01}_{1.7E-1}$	$6.5E-{01}_{1.7E-1}$	$8.8E-{01}_{2.6E-1}$	$4.2E-{01}_{2.0E-1}$	$8.9E-{01}_{1.9E-1}$	$1.1E{00}_{2.8E-1}$	$6.7E-{01}_{1.8E-1}$	$7.7E-{01}_{1.9E-1}$
Sparse-64-96-0.75-1	$2.1E-{01}_{7.4E-2}$	$1.8E-{01}_{5.7E-2}$	$3.1E-{01}_{6.5E-2}$	$1.4E-{01}_{6.2E-2}$	$3.3E-{01}_{9.3E-2}$	$3.3E-{01}_{8.1E-2}$	$1.8E-{01}_{5.7E-2}$	$2.8E-{01}_{5.8E-2}$
Sparse-64-96-0.75-5	$1.2E{00}_{5.8E-1}$	$1.2E{00}_{5.0E-1}$	$2.6E{00}_{5.1E-1}$	$9.3E-{01}_{4.4E-1}$	$3.2E{00}_{5.6E-1}$	$2.0E{00}_{4.3E-1}$	$1.3E{00}_{5.0E-1}$	$2.0E{00}_{7.7E-1}$
Sparse-64-96-0.75-10	$1.1E{02}_{7.9E1}$	$1.4E{02}_{7.8E1}$	$1.4E{02}_{7.4E1}$	$8.2E{01}_{6.9E1}$	$2.0E{02}_{9.3E1}$	$2.6E{02}_{9.4E1}$	$1.3E{02}_{6.7E1}$	$1.3E{02}_{7.8E1}$
Sparse-64-96-1-0.1	$2.5E-{01}_{7.5E-2}$	$2.6E-{01}_{5.8E-2}$	$4.1E-{01}_{4.5E-2}$	$1.5E-{01}_{6.9E-2}$	$3.9E-{01}_{8.9E-2}$	$3.8E-{01}_{8.1E-2}$	$2.4E-{01}_{6.2E-2}$	$3.8E-{01}_{6.7E-2}$
Sparse-64-96-1-0.5	$3.0E-{01}_{1.2E-1}$	$3.0E-{01}_{1.2E-1}$	$4.4E-{01}_{1.1E-1}$	$2.2E-{01}_{1.1E-1}$	$4.5E-{01}_{1.4E-1}$	$5.2E-{01}_{1.7E-1}$	$3.2E-{01}_{1.1E-1}$	$4.1E-{01}_{1.3E-1}$
Sparse-64-96-1-1	$4.1E-{01}_{1.6E-1}$	$4.3E-{01}_{1.4E-1}$	$5.1E-{01}_{1.4E-1}$	$3.3E-{01}_{1.4E-1}$	$5.8E-{01}_{1.6E-1}$	$7.3E-{01}_{1.6E-1}$	$4.4E-{01}_{1.5E-1}$	$4.8E-{01}_{1.4E-1}$
Sparse-64-96-1-5	$7.1E-{01}_{2.4E-1}$	$8.0E-{01}_{2.4E-1}$	$1.1E{00}_{2.6E-1}$	$4.8E-{01}_{2.0E-1}$	$1.3E{00}_{2.2E-1}$	$1.1E{00}_{2.5E-1}$	$6.9E-{01}_{2.9E-1}$	$1.0E{00}_{2.5E-1}$
Sparse-64-96-1-10	$5.3E-{01}_{1.3E-1}$	$5.9E-{01}_{1.6E-1}$	$9.6E-{01}_{2.4E-1}$	$3.8E-{01}_{1.7E-1}$	$1.1E{00}_{3.1E-1}$	$9.5E-{01}_{2.9E-1}$	$6.2E-{01}_{1.7E-1}$	$8.1E-{01}_{1.7E-1}$

,

Table 11. Epsilon+ mean and standard deviation of the eight algorithms over 30 independent runs. Dark/light gray emphasize the best/second-best results.

Problem	AGEMOEA	AGEMOEA2	GWASFGA	MOCell	MOMBI	MOMBI2	NSGA2	SMS-EMOA
Fpppp-8-334-0.1-0.1	$8.5E-{01}_{1.7E-1}$	$8.2E-{01}_{1.4E-1}$	$1.2E{00}_{1.7E-1}$	$5.8E-{01}_{2.0E-1}$	$1.1E{00}_{1.7E-1}$	$1.1E{00}_{1.4E-1}$	$8.4E-{01}_{1.9E-1}$	$1.1E{00}_{1.6E-1}$
Fpppp-8-334-0.1-0.5	$7.9E-{01}_{1.7E-1}$	$7.2E-{01}_{1.9E-1}$	$1.2E{00}_{1.9E-1}$	$6.6E-{01}_{1.9E-1}$	$1.1E{00}_{1.8E-1}$	$1.3E{00}_{2.6E-1}$	$8.0E-{01}_{1.7E-1}$	$1.1E{00}_{2.0E-1}$
Fpppp-8-334-0.1-1	$9.2E-{01}_{1.6E-1}$	$1.1E{00}_{1.8E-1}$	$1.3E{00}_{2.2E-1}$	$7.6E-{01}_{2.0E-1}$	$1.4E{00}_{1.9E-1}$	$1.3E{00}_{1.9E-1}$	$9.7E-{01}_{2.2E-1}$	$1.3E{00}_{2.4E-1}$
Fpppp-8-334-0.1-5	$3.6E{00}_{9.2E-1}$	$3.4E{00}_{1.1E0}$	$4.5E{00}_{7.0E-1}$	$3.3E{00}_{1.4E0}$	$4.3E{00}_{8.6E-1}$	$4.5E{00}_{9.6E-1}$	$3.5E{00}_{1.2E0}$	$4.0E{00}_{1.1E0}$
Fpppp-8-334-0.1-10	$6.5E{00}_{2.6E0}$	$7.7E{00}_{3.9E0}$	$1.1E{01}_{3.5E0}$	$6.8E{00}_{3.4E0}$	$9.2E{00}_{3.3E0}$	$8.8E{00}_{2.4E0}$	$6.5E{00}_{3.2E0}$	$8.2E{00}_{2.9E0}$
Fpppp-8-334-0.25-0.1	$9.9E-{01}_{2.4E-1}$	$1.0E{00}_{2.5E-1}$	$1.4E{00}_{2.2E-1}$	$7.8E-{01}_{2.9E-1}$	$1.3E{00}_{2.4E-1}$	$1.6E{00}_{2.8E-1}$	$1.0E{00}_{2.4E-1}$	$1.3E{00}_{2.0E-1}$
Fpppp-8-334-0.25-0.5	$5.1E-{01}_{1.2E-1}$	$5.3E-{01}_{1.0E-1}$	$7.0E-{01}_{9.0E-2}$	$4.5E-{01}_{1.4E-1}$	$6.8E-{01}_{8.3E-2}$	$7.4E-{01}_{1.1E-1}$	$5.3E-{01}_{6.7E-2}$	$6.6E-{01}_{8.6E-2}$
Fpppp-8-334-0.25-1	$2.1E{00}_{5.4E-1}$	$2.1E{00}_{8.2E-1}$	$2.8E{00}_{8.4E-1}$	$1.9E{00}_{6.6E-1}$	$3.0E{00}_{7.0E-1}$	$3.6E{00}_{7.0E-1}$	$2.1E{00}_{6.4E-1}$	$3.1E{00}_{6.9E-1}$
Fpppp-8-334-0.25-5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-8-334-0.25-10	$3.5E{00}_{1.3E0}$	$3.2E{00}_{1.6E0}$	$3.6E{00}_{1.4E0}$	$4.4E{00}_{2.1E0}$	$3.8E{00}_{1.8E0}$	$3.5E{00}_{1.4E0}$	$3.0E{00}_{1.5E0}$	$3.7E{00}_{1.5E0}$
Fpppp-8-334-0.5-0.1	$6.2E-{01}_{1.4E-1}$	$6.1E-{01}_{1.2E-1}$	$7.8E-{01}_{1.4E-1}$	$5.6E-{01}_{1.2E-1}$	$7.7E-{01}_{1.3E-1}$	$7.9E-{01}_{1.1E-1}$	$5.9E-{01}_{1.1E-1}$	$7.7E-{01}_{1.2E-1}$
Fpppp-8-334-0.5-0.5	$9.5E-{01}_{3.2E-1}$	$9.6E-{01}_{2.4E-1}$	$1.4E{00}_{2.7E-1}$	$7.3E-{01}_{3.2E-1}$	$1.4E{00}_{2.8E-1}$	$1.2E{00}_{2.8E-1}$	$9.5E-{01}_{3.3E-1}$	$1.4E{00}_{3.5E-1}$
Fpppp-8-334-0.5-1	$1.2E{00}_{2.5E-1}$	$1.2E{00}_{2.2E-1}$	$1.6E{00}_{1.8E-1}$	$9.5E-{01}_{2.2E-1}$	$1.5E{00}_{1.7E-1}$	$1.5E{00}_{2.6E-1}$	$1.1E{00}_{2.7E-1}$	$1.4E{00}_{2.6E-1}$
Fpppp-8-334-0.5-5	$4.4E{00}_{1.8E0}$	$4.2E{00}_{2.1E0}$	$5.2E{00}_{1.4E0}$	$4.8E{00}_{3.0E0}$	$5.8E{00}_{1.7E0}$	$5.9E{00}_{1.1E0}$	$4.1E{00}_{1.7E0}$	$4.7E{00}_{1.9E0}$
Fpppp-8-334-0.5-10	$8.2E{00}_{3.6E0}$	$8.6E{00}_{3.6E0}$	$1.0E{01}_{3.2E0}$	$9.6E{00}_{3.9E0}$	$1.0E{01}_{3.3E0}$	$1.1E{01}_{2.7E0}$	$8.5E{00}_{4.0E0}$	$9.2E{00}_{3.4E0}$
Fpppp-8-334-0.75-0.1	$1.3E{00}_{4.7E-1}$	$1.2E{00}_{4.2E-1}$	$2.0E{00}_{4.9E-1}$	$1.2E{00}_{5.2E-1}$	$1.6E{00}_{3.7E-1}$	$2.2E{00}_{4.5E-1}$	$1.4E{00}_{4.7E-1}$	$1.7E{00}_{4.5E-1}$
Fpppp-8-334-0.75-0.5	$7.1E-{01}_{1.9E-1}$	$7.6E-{01}_{1.5E-1}$	$9.0E-{01}_{2.2E-1}$	$7.4E-{01}_{2.2E-1}$	$9.0E-{01}_{1.9E-1}$	$8.9E-{01}_{1.8E-1}$	$7.1E-{01}_{2.0E-1}$	$9.6E-{01}_{2.0E-1}$
Fpppp-8-334-0.75-1	$1.5E{00}_{3.4E-1}$	$1.4E{00}_{5.1E-1}$	$2.0E{00}_{3.9E-1}$	$1.5E{00}_{4.8E-1}$	$2.1E{00}_{4.0E-1}$	$1.7E{00}_{4.2E-1}$	$1.5E{00}_{4.5E-1}$	$1.9E{00}_{4.7E-1}$
Fpppp-8-334-0.75-5	$1.0E{01}_{3.6E0}$	$1.0E{01}_{4.7E0}$	$1.5E{01}_{5.1E0}$	$9.9E{00}_{4.5E0}$	$1.4E{01}_{4.0E0}$	$1.4E{01}_{4.3E0}$	$1.2E{01}_{4.1E0}$	$1.3E{01}_{3.6E0}$
Fpppp-8-334-0.75-10	$3.9E{00}_{1.8E0}$	$4.2E{00}_{2.2E0}$	$5.7E{00}_{2.2E0}$	$5.9E{00}_{2.7E0}$	$5.4E{00}_{1.8E0}$	$4.6E{00}_{1.2E0}$	$4.3E{00}_{1.7E0}$	$4.0E{00}_{1.3E0}$
Fpppp-8-334-1-0.1	$2.9E{00}_{1.0E0}$	$2.4E{00}_{9.1E-1}$	$4.5E{00}_{1.2E0}$	$3.4E{00}_{1.5E0}$	$4.1E{00}_{9.9E-1}$	$4.4E{00}_{1.1E0}$	$2.3E{00}_{8.8E-1}$	$3.8E{00}_{1.2E0}$
Fpppp-8-334-1-0.5	$1.1E{00}_{2.8E-1}$	$8.9E-{01}_{3.1E-1}$	$1.6E{00}_{2.7E-1}$	$1.1E{00}_{3.3E-1}$	$1.3E{00}_{2.4E-1}$	$1.4E{00}_{2.2E-1}$	$9.4E-{01}_{3.3E-1}$	$1.2E{00}_{2.2E-1}$
Fpppp-8-334-1-1	$1.2E{00}_{2.9E-1}$	$1.1E{00}_{2.8E-1}$	$1.6E{00}_{2.4E-1}$	$1.1E{00}_{3.7E-1}$	$1.5E{00}_{2.6E-1}$	$1.5E{00}_{2.4E-1}$	$1.1E{00}_{2.8E-1}$	$1.4E{00}_{2.3E-1}$
Fpppp-8-334-1-5	$3.9E{00}_{1.7E0}$	$4.4E{00}_{1.6E0}$	$4.8E{00}_{1.4E0}$	$5.3E{00}_{1.8E0}$	$5.5E{00}_{1.7E0}$	$5.6E{00}_{1.8E0}$	$4.5E{00}_{1.2E0}$	$5.1E{00}_{1.2E0}$
Fpppp-8-334-1-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-16-334-0.1-0.1	$1.0E{00}_{2.7E-1}$	$9.1E-{01}_{2.6E-1}$	$1.6E{00}_{2.5E-1}$	$7.4E-{01}_{2.6E-1}$	$1.5E{00}_{2.7E-1}$	$1.7E{00}_{3.0E-1}$	$9.5E-{01}_{2.4E-1}$	$1.4E{00}_{2.8E-1}$
Fpppp-16-334-0.1-0.5	$2.3E{01}_{8.8E0}$	$2.7E{01}_{6.4E0}$	$3.4E{01}_{8.3E0}$	$1.8E{01}_{7.0E0}$	$3.8E{01}_{7.4E0}$	$3.5E{01}_{8.2E0}$	$2.8E{01}_{6.6E0}$	$4.0E{01}_{7.8E0}$
Fpppp-16-334-0.1-1	$7.5E{00}_{2.6E0}$	$7.4E{00}_{2.5E0}$	$1.2E{01}_{1.8E0}$	$5.9E{00}_{2.1E0}$	$1.2E{01}_{1.9E0}$	$1.2E{01}_{2.3E0}$	$6.3E{00}_{2.2E0}$	$1.0E{01}_{2.4E0}$
Fpppp-16-334-0.1-5	$7.9E{00}_{2.1E0}$	$7.9E{00}_{2.0E0}$	$1.0E{01}_{2.2E0}$	$7.9E{00}_{2.4E0}$	$9.5E{00}_{2.4E0}$	$9.6E{00}_{2.3E0}$	$7.0E{00}_{2.1E0}$	$9.8E{00}_{2.0E0}$
Fpppp-16-334-0.1-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-16-334-0.25-0.1	$1.3E{00}_{3.5E-1}$	$1.6E{00}_{2.3E-1}$	$2.1E{00}_{3.6E-1}$	$1.0E{00}_{3.3E-1}$	$2.0E{00}_{2.5E-1}$	$1.9E{00}_{3.1E-1}$	$1.3E{00}_{3.1E-1}$	$1.8E{00}_{3.7E-1}$
Fpppp-16-334-0.25-0.5	$1.5E{00}_{3.0E-1}$	$1.5E{00}_{3.4E-1}$	$2.4E{00}_{3.6E-1}$	$1.2E{00}_{4.1E-1}$	$2.2E{00}_{4.1E-1}$	$2.5E{00}_{4.4E-1}$	$1.6E{00}_{2.9E-1}$	$2.0E{00}_{3.2E-1}$
Fpppp-16-334-0.25-1	$2.9E{00}_{1.1E0}$	$3.0E{00}_{1.0E0}$	$4.9E{00}_{9.7E-1}$	$2.2E{00}_{7.9E-1}$	$4.4E{00}_{9.7E-1}$	$4.9E{00}_{1.1E0}$	$2.8E{00}_{9.6E-1}$	$4.5E{00}_{1.3E0}$
Fpppp-16-334-0.25-5	$9.9E{00}_{4.2E0}$	$9.8E{00}_{3.9E0}$	$1.3E{01}_{4.5E0}$	$1.2E{01}_{3.9E0}$	$1.2E{01}_{4.7E0}$	$1.1E{01}_{3.5E0}$	$1.0E{01}_{4.2E0}$	$1.0E{01}_{3.3E0}$
Fpppp-16-334-0.25-10	$3.8E{01}_{3.0E1}$	$4.3E{01}_{2.8E1}$	$4.0E{01}_{1.6E1}$	$5.4E{01}_{3.3E1}$	$5.0E{01}_{2.7E1}$	$4.0E{01}_{1.5E1}$	$3.6E{01}_{2.2E1}$	$5.2E{01}_{3.3E1}$
Fpppp-16-334-0.5-0.1	$1.5E{00}_{3.4E-1}$	$1.5E{00}_{3.6E-1}$	$2.4E{00}_{4.6E-1}$	$1.3E{00}_{4.6E-1}$	$2.3E{00}_{4.1E-1}$	$2.6E{00}_{4.3E-1}$	$1.6E{00}_{4.3E-1}$	$2.3E{00}_{4.1E-1}$
Fpppp-16-334-0.5-0.5	$1.3E{00}_{3.0E-1}$	$1.2E{00}_{3.1E-1}$	$1.9E{00}_{2.4E-1}$	$1.0E{00}_{2.6E-1}$	$1.9E{00}_{3.0E-1}$	$1.9E{00}_{2.7E-1}$	$1.3E{00}_{3.2E-1}$	$1.8E{00}_{2.2E-1}$
Fpppp-16-334-0.5-1	$1.8E{00}_{4.2E-1}$	$1.7E{00}_{5.0E-1}$	$2.6E{00}_{4.6E-1}$	$1.4E{00}_{4.5E-1}$	$2.5E{00}_{5.2E-1}$	$2.8E{00}_{4.9E-1}$	$1.9E{00}_{4.6E-1}$	$2.5E{00}_{5.3E-1}$
Fpppp-16-334-0.5-5	$3.2E{00}_{1.6E0}$	$2.8E{00}_{1.1E0}$	$4.1E{00}_{1.4E0}$	$3.2E{00}_{1.3E0}$	$3.8E{00}_{1.3E0}$	$3.9E{00}_{1.2E0}$	$2.7E{00}_{1.1E0}$	$4.0E{00}_{1.8E0}$
Fpppp-16-334-0.5-10	$8.8E{00}_{4.5E0}$	$8.7E{00}_{4.6E0}$	$1.3E{01}_{4.7E0}$	$1.5E{01}_{5.4E0}$	$1.4E{01}_{4.4E0}$	$1.1E{01}_{3.7E0}$	$9.0E{00}_{4.5E0}$	$1.3E{01}_{6.1E0}$
Fpppp-16-334-0.75-0.1	$1.1E{01}_{5.9E0}$	$1.2E{01}_{6.3E0}$	$2.0E{01}_{6.6E0}$	$1.1E{01}_{5.8E0}$	$2.2E{01}_{5.5E0}$	$1.7E{01}_{6.6E0}$	$1.1E{01}_{6.8E0}$	$1.8E{01}_{6.5E0}$
Fpppp-16-334-0.75-0.5	$2.0E{00}_{4.6E-1}$	$1.9E{00}_{4.2E-1}$	$2.5E{00}_{4.4E-1}$	$1.6E{00}_{5.3E-1}$	$2.8E{00}_{5.5E-1}$	$2.4E{00}_{4.3E-1}$	$2.2E{00}_{5.0E-1}$	$2.6E{00}_{4.7E-1}$
Fpppp-16-334-0.75-1	$8.7E{00}_{2.0E0}$	$8.5E{00}_{3.2E0}$	$1.3E{01}_{3.7E0}$	$7.4E{00}_{3.7E0}$	$1.4E{01}_{3.7E0}$	$1.3E{01}_{4.2E0}$	$8.6E{00}_{4.4E0}$	$1.4E{01}_{4.0E0}$
Fpppp-16-334-0.75-5	$5.5E{01}_{2.2E1}$	$5.4E{01}_{2.0E1}$	$6.7E{01}_{2.2E1}$	$5.5E{01}_{2.1E1}$	$7.5E{01}_{1.9E1}$	$7.4E{01}_{2.1E1}$	$5.4E{01}_{2.0E1}$	$7.6E{01}_{2.1E1}$
Fpppp-16-334-0.75-10	$1.2E{02}_{7.0E1}$	$1.2E{02}_{6.8E1}$	$1.7E{02}_{4.3E1}$	$1.7E{02}_{7.5E1}$	$1.6E{02}_{5.2E1}$	$1.7E{02}_{5.6E1}$	$1.2E{02}_{7.5E1}$	$1.4E{02}_{7.8E1}$
Fpppp-16-334-1-0.1	$2.3E{00}_{8.3E-1}$	$2.3E{00}_{8.0E-1}$	$4.2E{00}_{8.4E-1}$	$2.6E{00}_{7.9E-1}$	$3.7E{00}_{7.4E-1}$	$3.5E{00}_{8.2E-1}$	$2.3E{00}_{1.0E0}$	$3.6E{00}_{9.0E-1}$
Fpppp-16-334-1-0.5	$1.6E{00}_{4.8E-1}$	$1.7E{00}_{5.7E-1}$	$2.5E{00}_{4.9E-1}$	$1.6E{00}_{6.6E-1}$	$2.4E{00}_{5.0E-1}$	$2.4E{00}_{5.0E-1}$	$1.5E{00}_{6.1E-1}$	$2.2E{00}_{5.9E-1}$
Fpppp-16-334-1-1	$9.7E{00}_{2.5E0}$	$9.5E{00}_{4.6E0}$	$1.3E{01}_{3.1E0}$	$8.1E{00}_{3.6E0}$	$1.3E{01}_{2.9E0}$	$1.2E{01}_{2.8E0}$	$8.1E{00}_{3.1E0}$	$1.3E{01}_{2.8E0}$
Fpppp-16-334-1-5	$2.9E{01}_{9.1E0}$	$2.7E{01}_{8.5E0}$	$3.3E{01}_{9.0E0}$	$3.1E{01}_{1.1E1}$	$3.2E{01}_{1.2E1}$	$2.9E{01}_{1.1E1}$	$2.8E{01}_{1.1E1}$	$2.7E{01}_{1.0E1}$
Fpppp-16-334-1-10	$1.5E{01}_{5.4E0}$	$1.4E{01}_{4.9E0}$	$1.6E{01}_{5.4E0}$	$1.7E{01}_{6.4E0}$	$1.7E{01}_{4.7E0}$	$1.7E{01}_{5.1E0}$	$1.3E{01}_{6.0E0}$	$1.4E{01}_{4.7E0}$
Fpppp-32-334-0.1-0.1	$2.2E{00}_{8.6E-1}$	$2.2E{00}_{6.1E-1}$	$3.9E{00}_{8.0E-1}$	$1.5E{00}_{7.8E-1}$	$3.6E{00}_{9.6E-1}$	$3.9E{00}_{7.5E-1}$	$2.2E{00}_{7.3E-1}$	$3.5E{00}_{8.8E-1}$
Fpppp-32-334-0.1-0.5	$2.3E{00}_{6.3E-1}$	$2.5E{00}_{7.1E-1}$	$3.7E{00}_{7.1E-1}$	$1.6E{00}_{6.6E-1}$	$3.7E{00}_{6.1E-1}$	$3.7E{00}_{7.7E-1}$	$2.5E{00}_{6.4E-1}$	$3.7E{00}_{7.9E-1}$
Fpppp-32-334-0.1-1	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-0.1-5	$3.6E{00}_{1.3E0}$	$3.6E{00}_{1.2E0}$	$4.6E{00}_{1.3E0}$	$3.1E{00}_{1.4E0}$	$4.7E{00}_{1.2E0}$	$4.6E{00}_{1.2E0}$	$3.6E{00}_{1.5E0}$	$4.3E{00}_{1.2E0}$
Fpppp-32-334-0.1-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-0.25-0.1	$4.4E{00}_{1.1E0}$	$5.2E{00}_{1.5E0}$	$8.5E{00}_{1.3E0}$	$4.4E{00}_{1.5E0}$	$7.6E{00}_{1.3E0}$	$7.9E{00}_{1.5E0}$	$4.9E{00}_{1.2E0}$	$7.7E{00}_{1.5E0}$
Fpppp-32-334-0.25-0.5	$6.5E{00}_{3.0E0}$	$7.3E{00}_{2.4E0}$	$1.4E{01}_{3.2E0}$	$5.7E{00}_{2.5E0}$	$1.2E{01}_{2.7E0}$	$1.5E{01}_{2.6E0}$	$7.4E{00}_{2.4E0}$	$1.3E{01}_{2.8E0}$
Fpppp-32-334-0.25-1	$6.5E{01}_{1.7E1}$	$6.6E{01}_{1.6E1}$	$9.6E{01}_{1.5E1}$	$5.6E{01}_{2.2E1}$	$8.8E{01}_{1.4E1}$	$9.3E{01}_{1.9E1}$	$6.6E{01}_{1.8E1}$	$8.8E{01}_{1.7E1}$
Fpppp-32-334-0.25-5	$1.8E{01}_{5.0E0}$	$1.7E{01}_{5.5E0}$	$2.4E{01}_{5.8E0}$	$1.8E{01}_{5.3E0}$	$2.4E{01}_{5.4E0}$	$2.3E{01}_{5.8E0}$	$2.0E{01}_{4.3E0}$	$2.1E{01}_{6.4E0}$
Fpppp-32-334-0.25-10	$2.3E{01}_{9.8E0}$	$2.0E{01}_{1.0E1}$	$3.1E{01}_{8.6E0}$	$2.5E{01}_{7.6E0}$	$3.3E{01}_{1.1E1}$	$2.9E{01}_{1.1E1}$	$2.3E{01}_{9.4E0}$	$2.9E{01}_{9.9E0}$
Fpppp-32-334-0.5-0.1	$2.0E{00}_{5.3E-1}$	$1.7E{00}_{4.7E-1}$	$2.8E{00}_{6.2E-1}$	$1.4E{00}_{6.2E-1}$	$2.9E{00}_{5.7E-1}$	$2.9E{00}_{5.7E-1}$	$1.7E{00}_{5.2E-1}$	$2.8E{00}_{5.0E-1}$
Fpppp-32-334-0.5-0.5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-0.5-1	$4.8E{01}_{1.6E1}$	$4.6E{01}_{1.9E1}$	$7.5E{01}_{2.1E1}$	$4.7E{01}_{1.9E1}$	$7.3E{01}_{1.7E1}$	$8.1E{01}_{1.5E1}$	$4.7E{01}_{1.9E1}$	$7.4E{01}_{1.4E1}$
Fpppp-32-334-0.5-5	$3.8E{01}_{1.5E1}$	$3.8E{01}_{1.5E1}$	$4.2E{01}_{1.6E1}$	$3.9E{01}_{1.6E1}$	$5.4E{01}_{1.2E1}$	$4.6E{01}_{1.6E1}$	$3.9E{01}_{1.8E1}$	$4.3E{01}_{1.6E1}$
Fpppp-32-334-0.5-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-0.75-0.1	$5.8E{00}_{2.4E0}$	$5.8E{00}_{2.8E0}$	$1.0E{01}_{2.1E0}$	$4.9E{00}_{2.2E0}$	$1.1E{01}_{1.9E0}$	$9.6E{00}_{2.8E0}$	$5.6E{00}_{2.7E0}$	$1.0E{01}_{2.6E0}$
Fpppp-32-334-0.75-0.5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-0.75-1	$2.0E{00}_{6.3E-1}$	$1.6E{00}_{6.1E-1}$	$2.6E{00}_{8.1E-1}$	$1.6E{00}_{6.4E-1}$	$2.5E{00}_{6.6E-1}$	$2.7E{00}_{4.8E-1}$	$1.6E{00}_{7.7E-1}$	$2.7E{00}_{6.5E-1}$
Fpppp-32-334-0.75-5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-0.75-10	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-1-0.1	$2.3E{00}_{7.5E-1}$	$2.4E{00}_{8.4E-1}$	$3.7E{00}_{7.2E-1}$	$2.7E{00}_{9.4E-1}$	$3.7E{00}_{9.9E-1}$	$4.1E{00}_{1.0E0}$	$2.4E{00}_{8.2E-1}$	$3.6E{00}_{6.2E-1}$
Fpppp-32-334-1-0.5	$4.8E{00}_{1.6E0}$	$5.3E{00}_{1.4E0}$	$7.5E{00}_{1.3E0}$	$4.5E{00}_{1.8E0}$	$7.2E{00}_{1.5E0}$	$7.1E{00}_{1.6E0}$	$5.1E{00}_{1.5E0}$	$6.6E{00}_{1.6E0}$
Fpppp-32-334-1-1	$2.7E{00}_{9.0E-1}$	$2.7E{00}_{1.0E0}$	$4.2E{00}_{9.0E-1}$	$2.9E{00}_{9.3E-1}$	$3.9E{00}_{9.6E-1}$	$4.3E{00}_{1.0E0}$	$2.2E{00}_{7.1E-1}$	$3.8E{00}_{1.1E0}$
Fpppp-32-334-1-5	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$	$0.0E{00}_{0.0E0}$
Fpppp-32-334-1-10	$3.3E{01}_{1.4E1}$	$3.1E{01}_{1.4E1}$	$4.7E{01}_{1.2E1}$	$3.3E{01}_{1.0E1}$	$4.3E{01}_{1.1E1}$	$4.4E{01}_{1.4E1}$	$3.6E{01}_{1.1E1}$	$4.1E{01}_{1.1E1}$
Fpppp-64-334-0.1-0.1	$1.5E{00}_{3.5E-1}$	$1.5E{00}_{3.7E-1}$	$2.4E{00}_{3.6E-1}$	$1.2E{00}_{3.6E-1}$	$2.6E{00}_{4.0E-1}$	$2.3E{00}_{5.3E-1}$	$1.6E{00}_{3.6E-1}$	$2.3E{00}_{3.7E-1}$
Fpppp-64-334-0.1-0.5	$2.3E{00}_{6.0E-1}$	$2.2E{00}_{6.4E-1}$	$3.8E{00}_{5.2E-1}$	$1.2E{00}_{5.3E-1}$	$3.9E{00}_{6.0E-1}$	$$

, Table 12, Table 13 and Table 14, shows the results regarding the hypervolume, inverted generational distance, and additive epsilon multi-objective quality indicator for the parallel applications of Fpppp, LIGO, Robot, and Sparse, respectively. At first glance, we can see algorithms resulting a hypervolume of $0.0$ in the results tables; those algorithms do not achieve points inside the extreme points of the reference front (the best and worst objective values). In other cases, the eight algorithms produce a hypervolume of $0.0$. The explanation for the above situation is that when computing the hypervolume, if one coordinate point is equal to 0, the volume produced by that point is zero. Since we are normalizing and inverting the Pareto front approximation, when a solution achieves the worst normalized value ($1.0$) when inversing ($1.0-1.0=0$), it is equal to zero, which makes total sense because it produces a zero volume of dominated solutions in the objective space. The above is why many hypervolume computations shift the reference point to avoid zeros, but not in our computations because we want to be as accurate as possible.

Regarding hypervolume (see Table 3), the Fortran double precision floating point application (Fpppp) is the most challenging for the eight studied algorithms. None of the studied algorithms achieved hypervolume for a total of 28 instances, namely, Fpppp-8-334-0.25-5, Fpppp-8-334-0.75-5, Fpppp-8-334-1-10, Fpppp-16-334-0.1-0.5, Fpppp-16-334-0.1-10, Fpppp-16-334-0.25-5, Fpppp-16-334-0.25-10, Fpppp-16-334-0.75-5, Fpppp-16-334-0.75-10, Fpppp-16-334-1-5, Fpppp-32-334-0.1-1, Fpppp-32-334-0.1-10, Fpppp-32-334-0.25-0.1, Fpppp-32-334-0.25-0.5, Fpppp-32-334-0.25-1, Fpppp-32-334-0.25-5, Fpppp-32-334-0.5-0.5, Fpppp-32-334-0.5-1, Fpppp-32-334-0.5-5, Fpppp-32-334-0.5-10, Fpppp-32-334-0.75-0.5, Fpppp-32-334-0.75-5, Fpppp-32-334-0.75-10, Fpppp-32-334-1-0.5, Fpppp-32-334-1-5, Fpppp-64-334-0.5-0.1, Fpppp-64-334-0.75-0.1, and Fpppp-64-334-1-0.1. For some cases, one algorithm produces hypervolume, and the other seven do not; this is the case of NSGA2 in Fpppp-8-334-0.5-10, Fpppp-16-334-1-10, and Fpppp-64-334-0.25-10, and AGEMOEA2 in Fpppp-16-334-0.5-10. However, in the global count, MOCell outperforms all the studied algorithms by far, achieving the greatest number of best results in the Fpppp application.

The LIGO application instances are less challenging than Fpppp instances regarding the hypervolume (see Table 4), with six instances with no hypervolume for the eight studied algorithms, namely, LIGO-8-76-0.1-5, LIGO-16-76-0.5-5, LIGO-16-76-1-10, LIGO-32-76-0.5-1, LIGO-32-76-1-10, and LIGO-64-76-0.5-10. MOCell is the only algorithm achieving hypervolume in 10 instances (LIGO-16-76-0.25-0.1, LIGO-16-76-0.25-5, LIGO-16-76-0.75-5, LIGO-16-76-0.75-10, LIGO-32-76-0.25-5, LIGO-32-76-0.75-5, LIGO-64-76-0.1-5, LIGO-64-76-0.1-10, LIGO-64-76-0.25-10, and LIGO-64-76-1-10). Once again, MOCell achieved the greatest number of best results in the LIGO application instances.

The Robot application instances present a similar challenge to LIGO instances regarding the hypervolume (see Table 4), also having six instances with no hypervolume achieved for the eight studied algorithms, namely, Robot-8-88-0.5-5, Robot-8-88-0.5-10, Robot-8-88-1-10, Robot-16-88-0.5-5, Robot-16-88-0.5-10, and Robot-32-88-1-10. Again, MOCell achieved the greatest number of best results by far. The Sparse application instances are less challenging than the other real-world applications studied, with none producing zero hypervolume for all the studied algorithms. Once again, MOCell outperformed the studied algorithms with several best results.

Regarding the inverted generational distance plus (IGD+) results, we found an opposite situation as with hypervolume; for some instances, the IGD+ equals zero, meaning the solutions produced by the algorithms belong to the reference Pareto front, i.e., easy instances. A hypervolume of zero does not exclude the case of an IGD+ of zero. The above is the case of points in the nadir point of the reference front (the worst objective value). That solution would produce a hypervolume of zero and an IGD+ of zero because that point is inside the reference Pareto front. In a situation that contradicts the hypervolume results, the easier real-world application is Fpppp, according to IGD+ (see Table 7), with a total of thirteen instances with zero IGD+ for the eight studied algorithms, namely, Fpppp-8-334-0.25-5, Fpppp-8-334-1-10, Fpppp-16-334-0.1-10, Fpppp-32-334-0.1-1, Fpppp-32-334-0.1-10, Fpppp-32-334-0.5-0.5, Fpppp-32-334-0.5-10, Fpppp-32-334-0.75-0.5, Fpppp-32-334-0.75-5, Fpppp-32-334-0.75-10, Fpppp-32-334-1-5, Fpppp-64-334-0.75-0.1, and Fpppp-64-334-1-0.1. MOCell is the algorithm with the highest number of best results.

For the LIGO application IGD+ results (see Table 8), none of the instances achieved a zero IGD+ for all the studied algorithms. The algorithm with the best number of best results is MOCell. In the case of the Robot application IGD+ results, two instances achieved zero IGD+ for all the studied algorithms: Robot-8-88-0.5-5 and Robot-16-88-0.5-5. MOCell achieved most of the best results. For the Sparse application IGD+ results, none of the instances achieved a zero IGD+ for all the studied algorithms, and the algorithm with the greatest number of best results is MOCell again.

Regarding the Epsilon+ results, we found that the algorithms performed similarly to the IGD+ results. MOCell achieves the greatest number of best results for the four real-world applications (Fpppp, LIGO, Robot, and Sparse). The Fpppp instances are the easier ones according to Epsilon+ with thirteen instances with zero Epsilon+ (Fpppp-8-334-0.25-5, Fpppp-8-334-1-10, Fpppp-16-334-0.1-10, Fpppp-32-334-0.1-1, Fpppp-32-334-0.1-10, Fpppp-32-334-0.5-0.5, Fpppp-32-334-0.5-10, Fpppp-32-334-0.75-0.5, Fpppp-32-334-0.75-5, Fpppp-32-334-0.75-10, Fpppp-32-334-1-5, Fpppp-64-334-0.75-0.1, and Fpppp-64-334-1-0.1) and two Robot instances (Robot-8-88-0.5-5 and Robot-16-88-0.5-5). The best-performing algorithm in the four applications is MOCell.

Now, let us discuss the quality indicators results for the small synthetic benchmark. At first glance, we can observe that zero hypervolume, zero IGD+, and zero Epsilon+ do not happen in the computed results (see Table 15). The above gives us an insight into the convergence of the algorithms; a non-zero hypervolume means all the studied algorithms converge to solutions inside the reference Pareto front. Moreover, not finding zeros in the other quality indicators means a more well-distributed Pareto reference front than in real-world applications. Another relevant insight is that the worst algorithm for real-world applications (GWASFGA) is the best for the small synthetic benchmark.

The results in hypervolume for the real applications are supported by the Friedman statistical test results in Table 16 with a statistical significance of 95% (p-value $\le 0.05$). Regarding the hypervolume of real-world applications, the best-ranked algorithm is MOCell, followed by AGEMOEA, NSGA2, AGEMOA2, SMS-EMOA, MOMBI2, MOMBI, and GWASFGA.

The IGD+ results for the real applications are supported by the Friedman statistical test results in Table 16, with a statistical significance of 95% (p-value $\le 0.05$). Regarding the IGD+ of real-world applications, the best-ranked algorithm is MOCell, followed by AGEMOEA, AGEMOEA2, NSGA2, SMS-EMOA, MOMBI, MOMBI2, and GWASFGA.

The Epsilon+ results for the real applications are supported by the Friedman statistical test results in Table 16 with a statistical significance of 95% (p-value $\le 0.05$). The best-ranked algorithm for Epsilon+ of the real-world applications is MOCell, followed by AGEMOEA, AGEMOA2, NSGA2, SMS-EMOA, MOMBI, GWASFGA, and MOMBI2.

The GWASFGA superiority in the small synthetic benchmark is supported by the Friedman statistical test results in Table 17 with a statistical significance of 95% (p-value $\le 0.05$) for the quality indicators of hypervolume, IGD+, and Epsilon+. The best-ranked algorithms for the three quality indicators are GWASFGA followed by NSGA2, AGEMOA2, and AGEMOEA.

6.2. Graphical Results

To show the multi-objective nature of our study problem, we plot four Pareto front approximations: two approximations—according to the hypervolume quality indicator—of the best-performing algorithm for real-world applications, MOCell, and two approximations of the best-performing algorithm in the small synthetic benchmark, GWASFGA. As demonstrated by the graphical results in Figure 1, the problem studied in this work is multi-objective, with objectives in conflict, producing a set of optimal solutions instead of a single one. Solutions are non-dominated according to the Pareto optimality [42].

7. Conclusions

This paper deals with a new multi-objective formulation for precedence–constraint task scheduling on heterogeneous systems, known as High-Performance Computing (HPC) systems. We use two benchmarks to asses eight relevant, state-of-the-art algorithms: AGEMOEA, AGEMOEA2, GWASFGA, MOCell, MOMBI, MOMBI2, NSGA2, and SMS-EMOA. One benchmark consists of 400 instances of real-world parallel applications and one of 14 synthetic small instances in the literature. The best algorithm for the benchmark of real-world applications is MOCell, according to the computed Friedman average rankings, and the best algorithm for the small synthetic benchmark is GWASFGA.

We found contradictory results in the parallel applications about the difficulty of the studied instances according to the compute multi-objective quality indicators. A clear example of this contradiction is in Fpppp-8-334-0.25-5, which computes a zero hypervolume for the eight studied algorithms, zero inverted generational distance plus, and zero additive epsilon. The above contradictory situation does not happen in the small synthetic benchmark. The rationale behind the contradiction is the convergence of the algorithms and their produced reference Pareto front. The real-world applications have significantly more decision variables than the synthetic benchmark, and 25,000 function evaluations are insufficient to produce convergence for the studied algorithms, as Figure 1 reveals, producing contradictory results.

On the contrary, 25,000 function evaluations are enough for the eight studied algorithms to converge to similar Pareto front approximations in the small synthetic benchmark, producing hypervolume values > 0 and not zero in IGD+ and Epsilon+. However, MOCell performs remarkably against all the studied algorithms in real-world applications. The above could be explained because MOCell’s structured grid population neighborhoods give it more information to deal with the decision variables. In neighborhoods, solutions are closer to each other in the decision variable space—unlike the other studied algorithms, which relate the solutions only by their objective space. Therefore, MOCell scales better when the scheduling problem increases.

A drastic change in the ranking places occurs in the small synthetic benchmark, where GWASFGA outperforms all the other studied algorithms. MOCell is outperformed by at least four algorithms in every multi-objective quality indicator. The GWASFGA strategy of using the worst and best points as a reference may be why GWASFGA achieves the extreme points of the reference Pareto front easier than the other studied algorithms for the small synthetic benchmark. Hence, we recommend using GWASFGA for small scheduling problems and MOCell for bigger ones. Ours is the first study to consider switching off completely idle machines guided by the total energy consumption of the machines in the whole HPC system, even when idle. Our conclusions are within the multi-objective optimization field, and the conclusions are only valid for the produced Pareto front approximations when considering the studied algorithms for comparison.

For future research, we would like to compute better reference Pareto fronts to improve the accuracy of the quality indicators’ measures and avoid contradictory results.