Search Results (76)

Econometric estimation of panel data models by feasible generalized least squares (FGLS) provides an example of how conceptually simple problems may run into computational bottlenecks. I address the main computational tasks of FGLS within the R system for statistical computing, comparing different tools from the point of view of computational efficiency. I concentrate on estimating two models: the popular “random effects” with two error components and the less restrictive “general GLS” specification, which does not fit into the standard computational framework usually employed for the former. I compare the standard solution (partial time demeaning) with two alternative strategies, based respectively on algebraic properties and on object-oriented programming. I show how, while naive implementations become infeasible with large datasets, both list operators and object-oriented matrix routines available in the R environment make the problem tractable for most practically relevant sample sizes on any machine. I conclude by briefly discussing the parallelization of critical tasks. Full article

With growing interest in automated dismantling operations for hazardous environments, automatically planning safe and efficient disassembly sequences is becoming increasingly important. When a large structure is segmented into parts, the removal order must ensure that each part can be extracted safely without destabilising the remaining structure. This paper presents a comparative study of four algorithms for solving the disassembly sequencing problem in two dimensions: First Feasible Random Search (FFRS), Greedy Search (GS), Height-Decreasing Search (HDS), and Stochastic Tree Search (STS). The present study focuses specifically on sequencing feasibility under geometric and physical constraints, namely connectivity, accessibility, and structural stability. The 2D formulation provides a simplified yet computationally efficient testbed for analysing algorithmic behaviour under varying cutting complexities, with the objective of minimising the total removal trajectory length. Results show that while STS consistently finds optimal or near-optimal solutions, its factorial runtime limits scalability. GS produces high-quality solutions efficiently but can become trapped in infeasible configurations, whereas HDS offers strong reliability and speed at the expense of solution quality. Based on these findings, a hybrid height-based backtracking algorithm is proposed as a promising future direction, combining the efficiency of greedy search with the robustness of stochastic exploration. The results provide insight into the relative strengths and limitations of different sequencing strategies and establish a foundation for future extension to more realistic dismantling scenarios, including 3D and radiologically constrained applications. Full article

The optimal reconfiguration of electrical distribution feeders is a fundamental strategy for reducing active power losses and improving voltage profiles, yet it remains a challenging mixed-integer nonlinear programming (MINLP) problem due to the combinatorial explosion of radial topologies and the nonlinearities introduced by power flow equations. This paper proposes a novel master–slave methodology that integrates a Chu and Beasley genetic algorithm (CBGA) with a minimum spanning tree (MST)-based repair mechanism to address these challenges. In the master stage, the CBGA explores the binary space of switching decisions via steady-state population management, duplicate elimination, and stagnation restart policies. A key contribution lies in the MST-based repair procedure, which ensures that every individual generated by crossover and mutation is projected onto a feasible radial and connected configuration, effectively confining the search to the constrained solution space without recourse to penalty functions. A systematic weight-design rule preserves the Hamming distance between infeasible offspring and repaired solutions, minimizing the distortion of genetic information. The slave stage evaluates each candidate topology using a successive approximations power flow solver, assessing electrical feasibility and computing active power losses. The proposed methodology is validated on multiple test feeders, ranging from small 9- and 24-bus networks to large-scale benchmarks including 33-, 69-, 84-, 136-, and 415-bus systems. A comparison against the deterministic sequential switch opening method (SSOM) and a specialized tabu search demonstrates that the CBGA-MST consistently matches the best-known optima in the literature, achieving loss reductions of up to 9.63% compared to SSOM on the 415-bus system. A statistical analysis over 100 independent runs confirms the algorithm’s robustness, with zero standard deviation for networks of up to 69 buses and a standard deviation of only 2.99 kW (0.51%) for the 415-bus system. The findings confirm that the proposed approach offers superior scalability, robustness, and solution quality, positioning it as a practical and effective tool for distribution system operators seeking to enhance network efficiency under peak load conditions. Full article

The joint optimization of routing and three-dimensional loading is a highly complex NP-hard combinatorial problem, particularly when stringent center-of-gravity (CoG) stability constraints are required for unmanned aerial vehicle (UAV) operations. Existing algorithms typically adopt a route-first, load-second evaluation strategy for these interconnected components, often yielding distance-optimal yet physically infeasible solutions. To address this bottleneck, this paper formulates the Three-Dimensional Loading-Constrained UAV Routing Problem (3DLC-UAVRP), integrating unloading sequence consistency, spatial packing feasibility, and CoG deviation control into the routing decision process. A guided collaborative optimization framework, GLS-WSCPA, is proposed, coupling an Improved White Shark Optimization (IWSO) algorithm for global route exploration with a Human-like Divide-and-Conquer Packing Strategy (HLDCPS) for spatial arrangement. Unlike conventional decoupled approaches that treat loading feasibility as a post hoc filter, a Center-of-Gravity-Guided Path Adjustment (CGPA) and Local Loading Repair (LLR) mechanism is introduced to establish a dynamic feedback loop between routing search and loading evaluation, so that CoG violations are actively translated into guided routing perturbations rather than simply triggering solution rejection. Experimental results demonstrate that GLS-WSCPA generally achieves better solutions than the compared algorithms across the tested problem scales, with the performance gap tending to widen as the instance size increases within the tested range. Ablation studies verify the complementary roles of CGPA and LLR, and sensitivity analysis confirms that moderately relaxing payload and CoG constraints reduces routing distance within safety boundaries. Case analysis shows that the proposed method reduces fleet size by 20% and total delivery distance by 6.85% compared to traditional decoupled strategies. Full article

Low-carbon scheduling of regional integrated energy systems (RIES) based only on energy-balance models may overlook the physical operating limits of distribution, gas, and heating networks, resulting in a gap between scheduling outcomes and actual operating boundaries. To address this issue, this paper proposes a framework integrating bi-objective scheduling, three-network posterior verification, and closed-loop repair. A mixed-integer linear programming model is first formulated with operating cost and carbon emissions as the two objectives, and an adaptive ε-constraint strategy is used to improve the characterization of the compromise region on the Pareto front. Posterior verification models are then established for the distribution, gas, and heating networks to assess the physical feasibility of representative solutions. When infeasibility is detected, a boundary-shrinking repair mechanism is triggered to iteratively update the scheduling boundaries. Case results show that the adaptive refined strategy improves the resolution of the compromise region by 3.2 times with only a 20.4% increase in computational time. Compared with the cost-optimal solution, the carbon-optimal solution reduces carbon emissions but increases peak purchased electricity from 7.333 MW to 11.1 MW, further tightening the lower-voltage margin of the distribution network. The results show that posterior physical verification and closed-loop repair provide additional support for evaluating and improving the engineering feasibility of RIES scheduling solutions. Full article

Wireless sensor networks increasingly support time-critical monitoring applications, where coverage optimization must often be performed under limited computational resources. This work addresses a previously underexplored WSN coverage problem involving range-free, angular-limited sensors with transmitter-induced sensing degradation and discrete sector orientation. We formulate a mixed combinatorial problem that jointly optimizes $K$-out-of-$N$ sensor activation and sector assignment under strict feasibility constraints. A constraint-aware genetic algorithm with repair-based feasibility enforcement is proposed and validated against the global optimum obtained via exhaustive enumeration, enabling direct quantification of optimality. The repair mechanism corrects infeasible offspring after each genetic operation to guarantee that exactly K sensors remain active, eliminating the need for penalty-based constraint handling. A brute-force search is used to establish the global optimum of our small-scale scenario, serving as a ground-truth optimality benchmark for evaluating the proposed method. The purpose of this comparison is not to assess competitiveness against other metaheuristic algorithms, but to quantify how closely the proposed approach approximates the true optimal solution under strict problem constraints. The constraint-aware genetic algorithm is developed using an integer chromosome encoding, two initialization strategies, two crossover pairing schemes, elitism, and per-gene mutation, combined with alternative constraint-handling strategies. Two experimental series evaluate the impact of population size, crossover method, mutation probability, and constraint handling using problem-specific metrics, alongside convergence and fitness statistics. The proposed algorithm reliably reaches near-optimal solutions with significantly reduced computational cost when compared to exhaustive search. By integrating problem-specific constraints directly into the process, the proposed evolutionary optimization method effectively balances solution quality and execution time, making it well suited for scenarios requiring rapid sensor reconfiguration. Full article

Shape optimization of arch dams is essential for balancing structural safety and economic efficiency, yet remains computationally intensive due to costly finite element analyses and strict geometric constraints. Conventional sampling techniques often yield infeasible designs that undermine surrogate model fidelity. This study proposes an integrated framework combining Stratified Conditional Latin Hypercube Sampling (SC-LHS), automated modeling, and Gaussian Process (GP) surrogate models. SC-LHS incorporates hierarchical constraints to eliminate infeasible samples during generation, while a Python-driven workflow automates the process from parameterization to simulation. Coupling the GP surrogate with NSGA-II enables efficient Pareto front exploration. The results indicate that SC-LHS is superior to standard LHS, Constrained LHS, and Sobol sequences with rejection in terms of feasibility rate and space-filling metrics. The optimal compromise solution reduces dam volume by 10.4% and tensile zone volume by 15.2% compared to the initial design. This framework effectively reconciles economic and safety objectives, offering a robust methodology for complex hydraulic structure design. Full article

Autonomous path planning in obstacle-dense environments remains challenging for swarm intelligence methods due to infeasible initialization, insufficient exploration–exploitation balance, and poor trajectory smoothness for real-robot execution. To address these issues, this paper proposes a Kinematic-Aware Improved Hippo Optimization algorithm (KA-IHO) for mobile robot path planning. The proposed method integrates four components: an elite safety pool initialization strategy to improve feasible solution generation in dense maps, a hierarchical elite-scout update mechanism to better balance global exploration and local exploitation, anti-stagnation mechanisms including a Population Stagnation Restart strategy and a 10-Direction Radial Micro-Search to guarantee high feasibility rates across all map complexities, and a late-stage Laplacian Line-of-Sight Ironing Operator to reduce path redundancy and improve trajectory smoothness. Comparative experiments are conducted on five reproducible grid maps with different complexity levels ($40\times 40$ and $80\times 80$), where KA-IHO is evaluated against six representative algorithms, including HO, SBOA, PSO, GWO, ARO, and INFO, over 20 independent runs. The results show that KA-IHO consistently achieves collision-free planning and obtains lower mean fitness values with smaller standard deviations than the compared methods, indicating improved robustness and solution quality. In addition, hardware closed-loop experiments on a differential-drive mobile robot demonstrate that the planned paths can be executed reliably in real environments, with trajectory tracking errors controlled within $\pm 4$ cm. Full article

Solving constrained multiobjective optimization problems (CMOPs) is highly challenging due to the presence of complicated feasible regions, intense conflicts among objectives, and unevenly distributed constraints. As a result, conventional methods relying on a single constraint-handling mechanism frequently fail to maintain a stable equilibrium among solution feasibility, diversity, and convergence. To overcome these bottlenecks, this article introduces AFFCMO, a novel adaptive feasibility-guided framework tailored for constrained multiobjective optimization. At its core, the proposed approach utilizes a coevolutionary dual-population architecture that divides the search process into two distinct tasks. Specifically, an auxiliary population is tasked with global exploration, while a primary population focuses on the intensive exploitation of discovered feasible areas. To achieve this, the primary population leverages a DE/current-to-pbest/1 differential evolution strategy to closely approximate the constrained Pareto front. Simultaneously, the auxiliary population expands the search space using a mutation operator that adapts to the current evolutionary stage. Furthermore, exploration is bolstered by a multicriterion environmental selection scheme designed for the auxiliary group. By combining Euclidean geometric distributions, constraint relaxation, and value modeling inspired by epidemic dynamics, this strategy successfully preserves valuable infeasible solutions that can guide the search. Additionally, a dynamic resource allocation strategy based on historical search feedback and Thompson sampling is incorporated. This mechanism continuously evaluates the recent search contributions of both populations and adaptively adjusts their offspring sizes, thereby reducing the bias introduced by static allocation schemes. This mechanism continuously assesses the actual search contributions of both populations, allowing for the adaptive resizing of offspring generations and thereby eliminating the inherent biases of static allocation methods. Comprehensive empirical evaluations are conducted on 47 benchmark problems from four distinct test suites. The results indicate that AFFCMO significantly outperforms seven contemporary multiobjective evolutionary algorithms in terms of exploring complex feasible regions, preserving solution diversity, and achieving high convergence accuracy. Full article

Aiming at the structural lightweight design of a 700 mm aperture primary mirror for a space camera, a novel success history-based adaptive multi-objective differential evolution algorithm with dynamic constraint handling is proposed to solve the multi-objective optimization problem of simultaneously minimizing mass and compliance under strict constraints for surface error and first-order modal frequency. Firstly, a surrogate model for the mirror was constructed using the Kriging algorithm based on Optimal Latin Hypercube Sampling, establishing a mapping relationship between input design variables and output responses, thereby replacing computationally expensive finite element simulations. Subsequently, a dynamic constraint adjustment mechanism was introduced into the Success History-based Adaptive Multi-Object Differential Evolution algorithm for the surrogate model, dynamically relaxing and tightening constraint violation requirements during iteration. This allows for utilizing promising yet infeasible solutions for rapid convergence while ensuring the feasibility of the final solutions. Comparisons with 13 advanced constrained multi-objective optimization algorithms demonstrate that the proposed algorithm exhibits excellent convergence, diversity, and consistency. Finally, the optimal solution was selected from the Pareto front obtained by the proposed algorithm, and the design variable values were adjusted according to manufacturing constraints to yield the final optimization result, which was then verified by finite element simulation. The simulation results show that the final mirror structure meets all performance constraints, demonstrating the effectiveness and engineering applicability of the proposed algorithm for the structural lightweight design of space camera mirrors. Full article

The Set Covering Problem is a fundamental NP-hard problem in combinatorial optimization and plays a central role in a wide range of industrial decision-making processes, including logistics planning, scheduling, facility location, network design, and resource allocation. In many real-world contexts, problems of this type are large in scale and highly constrained, which makes exact solution methods computationally impractical and encourages the use of metaheuristic approaches capable of producing high-quality solutions within limited time budgets. In this work, we propose a discrete adaptation of the Dream Optimization Algorithm, focusing on the challenges that emerge when algorithms originally designed for continuous search spaces are applied to binary and strongly constrained models. The continuous search process is mapped onto the binary decision space through a fixed discretization scheme. As a consequence of this transformation, some constraints may not be met, underscoring the importance of effective feasibility restoration mechanisms. Because the discretization stage may produce infeasible solutions and frequently induces plateaus that hinder further improvement, an explicit repair phase becomes necessary to restore feasibility and promote effective search progression. To strengthen this process, the study introduces an adaptive control mechanism based on bandit driven operator selection, which dynamically chooses among different repair procedures during the search. Experimental results on benchmark instances show that the proposed approach consistently achieves high quality solutions with low relative deviation from known optima and stable behavior across independent runs. Full article

High-agility spacecraft require time-efficient attitude maneuvers under strict actuator- and system-driven saturation limits on angular rate and angular acceleration. Analytical methods for attitude profile generation are attractive for on-board use because of their deterministic structure and low computational burden; however, depending on boundary conditions and sequential constraint-enforcement logic, they may yield either infeasible commands that violate constraints or overly conservative commands that underutilize available authority and unnecessarily prolong maneuver time. In contrast, numerical optimization-based methods can produce (near-)minimum-time solutions but are often too iterative and tuning-sensitive for real-time deployment. The proposed method produces an iteratively refined closed-form solution. The inner loop yields a closed-form solution for a given set of parameters, while the outer loop updates the parameter set via an iterative rescale step. The resulting finite-jerk (jerk-limited) profiles are intended for use in a feedforward–feedback architecture to mitigate terminal mismatch induced by quaternion-kinematics linearization and acceleration-related variable mappings. Numerical studies evaluate the proposed method using representative single-case examples and Monte Carlo simulations with comparisons against a baseline analytical method and a numerical optimization-based method. These results indicate that the proposed approach substantially improves feasibility and optimality such that it achieves maneuver times close to those of numerically optimized solutions, while maintaining a semi-closed-form structure. Full article

In mine ventilation network calibration, sparse and inconsistent airflow measurements often lead to infeasibility in traditional optimization models. To overcome this challenge, this paper proposes a nonlinear programming calibration model incorporating slack variables. The model treats aerodynamic resistance corrections, airflow adjustments, unknown airflows, and resistance lower-bound slack variables as decision variables. The objective function is formulated to minimize the weighted sum of squares of resistance corrections, while penalty terms account for airflow adjustments and slack variables. Constraints integrate Kirchhoff’s laws with relaxed inequality constraints for resistance lower bounds. A calibration tool integrated via the ObjectARX interface was developed using C++, utilizing the Sequential Quadratic Programming (SQP) algorithm for the solution. The method was validated via a case study of a network comprising 39 branches and 16 measured airflows, optimized under five distinct initial conditions. Results demonstrate that the inclusion of slack variables mathematically guarantees the existence of feasible solutions. With a resistance correction weight of 10⁻² and a penalty coefficient of 10⁵, the model applies only minimal necessary corrections to handle overly tight constraints or data conflicts. The SQP algorithm exhibits superior global convergence, consistently iterating to optimal solutions that satisfy network balance laws regardless of initial values. This approach effectively resolves the infeasibility and data conflict issues inherent in traditional methods, demonstrating significant robustness and practical engineering utility. Full article

This paper addresses the Pickup and Delivery Problem with Time Windows (PDPTW), an NP-hard combinatorial optimization problem with major practical relevance in logistics and transportation. The study focuses on a quadratic unconstrained binary optimization (QUBO) formulation for quantum annealing and benchmarks it against two classical optimization paradigms. A modular Python framework is developed that encodes PDPTW in three ways: a mixed-integer linear programming (MILP) model that serves as an exact reference, a genetic algorithm (GA) metaheuristic, and a QUBO model that is compatible with quantum annealers. The framework supports test scenarios with increasing structural complexity, with both feasible and intentionally infeasible instances. An additional contribution is the conceptual design and preliminary analysis of an automatic-penalty weight-tuning scheme for the QUBO model. Experimental results show that the proposed QUBO formulation can produce high-quality solutions for simpler PDPTW instances, but its performance strongly depends on the careful calibration of penalty weights. MILP provides optimal baselines on small instances but becomes intractable as problem size grows. The GA scales to the largest scenario and finds feasible solutions of reasonable quality, but they are not necessarily optimal. The evaluation also includes a large number of problem instances and runs on IBM Quantum hardware using the Quantum Approximate Optimization Algorithm (QAOA). Full article

Constrained optimization problems (COPs) are frequent in engineering design yet remain challenging due to complex search spaces and strict feasibility requirements. Existing swarm-based optimizers often rely on penalty functions or algorithm-specific control parameters, whose performance is sensitive to problem-dependent tuning and may lead to premature convergence or infeasible solutions when feasible regions are narrow. This paper introduces the Constrained Team-Oriented Swarm Optimizer (CTOSO), a tuning-free metaheuristic that adapts the ETOSO framework by replacing linear exploiter movement with spiral search and integrating Deb’s feasibility rule. The population divides into Explorers, promoting diversity through neighbor-guided navigation, and Exploiters, performing intensified local search around the global best solution. Extensive evaluation on twelve constrained engineering benchmark problems shows that CTOSO achieves a 100% feasibility rate and attains the highest overall composite performance score among the compared algorithms under limited function-evaluation budgets. On the CEC 2017 constrained benchmark suite, CTOSO attains an average feasibility rate of 79.78%, generating feasible solutions on 14 out of 15 problems. Statistical analysis using Wilcoxon signed-rank tests and Friedman ranking with Nemenyi post hoc comparison indicates that CTOSO performs significantly better than several baseline optimizers, while exhibiting no statistically significant differences with leading evolutionary methods under the same experimental conditions. The algorithm’s design, requiring no tuning of algorithm-specific control parameters, makes it suitable for real-world engineering applications where tuning effort must be minimized. Full article