Search Results (36)

Surrogate-assisted evolutionary algorithms (SAEAs) have emerged as a powerful class of optimization methods that utilize surrogate models to address expensive optimization problems (EOPs), where fitness evaluations (FEs) are expensive or limited. By leveraging previously evaluated solutions to learn predictive models, SAEAs enable efficient search under constrained evaluation budgets. However, the performance of SAEAs heavily depends on the quality and utilization of surrogate models, and balancing the accuracy and generalization ability makes effective model construction and management a key challenge. Therefore, this paper introduces a novel probability selection-based surrogate-assisted evolutionary algorithm (PS-SAEA) to enhance optimization performance under FE-constrained conditions. The PS-SAEA has two novel designs. First, a probabilistic model selection (PMS) strategy is proposed to stochastically select surrogate models, striking a balance between prediction accuracy and generalization by avoiding overfitting commonly caused by greedy selection. Second, a weighted model ensemble (WME) mechanism is developed to integrate selected models, assigning weights based on individual prediction errors to improve the accuracy and reliability of fitness estimation. Extensive experiments on benchmark problems with varying dimensionalities demonstrate that PS-SAEA consistently outperforms several state-of-the-art SAEAs, validating its effectiveness and robustness in dealing with various complex EOPs. Full article

Lighting systems in sports venues have a significant impact on both the user experience and quality of events. However, owing to the large number of luminaires, high individual lamp power, and strict lighting standards, the lighting energy consumption of sports venues is high, accounting for approximately 30% of the total energy use. Therefore, introducing natural light through appropriate means during non-event periods and ensuring adequate lighting via collaborative control between natural light and artificial-lighting systems are crucial for reducing the lighting energy consumption of sports venues. Light pipe systems are a novel form of natural lighting and can effectively supplement artificial lighting. However, no clear methodology for selecting light pipes or designing light pipe systems in high spaces such as sports venues currently exists. Furthermore, developing a method for collaborative control between artificial-lighting systems and light pipe systems under various natural light conditions is an urgent issue in the optimization of the design of sports venue lighting. Therefore, we considered a conventional sports venue as a case study. By conducting HOLIGILM simulation experiments, we first investigated the factors affecting the transmission efficiency of light pipe systems and proposed optimization parameters for system design in terms of the pipe diameter, length, and configuration. Subsequently, using the Chinese Standard for Daylighting Design of Buildings (GB50033-2013) and the construction cost as optimization objectives, we optimized the pipe diameter, length, and placement of the light pipe system by applying non-dominated sorting genetic algorithm II. The simulation results showed that the optimized design of the light pipe system in the sports venue achieved a daylight factor of 1%, which met the standard requirements while reducing the construction cost by approximately 27%. Finally, to meet the indoor Class I (non-tournament) lighting standards stipulated in the Standard for Lighting Design and Test of Sports Venues (JGJ153-2016) and taking energy conservation as the optimization goal, we proposed a strategy for achieving collaborative control between the light pipe system and artificial-lighting system based on a greedy algorithm. The results indicated that under various weather conditions, the collaborative control strategy enabled the lighting of the field of play to meet Class I illuminance standards while reducing the annual lighting energy consumption by 35%. Thus, this study provides a methodological reference for optimizing the design of light pipe systems and achieving collaborative control with artificial-lighting systems in large-scale venues. Although these results were obtained based on meteorological data from Xi’an, China, the research method presented in this study can also be applied to other regions. The study provides a methodological reference for the design and optimization of light pipe systems and associated control systems to operate light pipes alongside artificial lighting systems in sports venues and other large multistory buildings. Full article

In the technology-assisted review (TAR) area, most research has focused on ranking effectiveness and active learning strategies within individual topics, often assuming unconstrained review effort. However, real-world applications such as legal discovery or medical systematic reviews are frequently subject to global screening budgets. In this paper, we revisit the CLEF eHealth TAR shared tasks (2017–2019) through the lens of budget-aware evaluation. We first reproduce and verify the official participant results, organizing them into a unified dataset for comparative analysis. Then, we introduce and assess four intuitive budget allocation strategies—even, proportional, inverse proportional, and threshold-capped greedy—to explore how review effort can be efficiently distributed across topics. To evaluate systems under resource constraints, we propose two cost-aware metrics: relevant found per cost unit (RFCU) and utility gain at budget (UG@B). These complement traditional recall by explicitly modeling efficiency and trade-offs between true and false positives. Our results show that different allocation strategies optimize different metrics: even and inverse proportional allocation favor recall, while proportional and capped strategies better maximize RFCU. UG@B remains relatively stable across strategies, reflecting its balanced formulation. A correlation analysis reveals that RFCU and UG@B offer distinct perspectives from recall, with varying alignment across years. Together, these findings underscore the importance of aligning evaluation metrics and allocation strategies with screening goals. We release all data and code to support reproducibility and future research on cost-sensitive TAR. Full article

The Slime Mould Algorithm (SMA) is a widely used swarm intelligence algorithm. Encouraged by the theory of no free lunch and the inherent shortcomings of the SMA, this work proposes a new variant of the SMA, called the BWSMA, in which three improvement mechanisms are integrated. The adaptive greedy mechanism is used to accelerate the convergence of the algorithm and avoid ineffective updates. The best–worst management strategy improves the quality of the population and increases its search capability. The stagnant replacement mechanism prevents the algorithm from falling into a local optimum by replacing stalled individuals. In order to verify the effectiveness of the proposed method, this paper conducts a full range of experiments on the CEC2018 test suite and the CEC2022 test suite and compares BWSMA with three derived algorithms, eight SMA variants, and eight other improved algorithms. The experimental results are analyzed using the Wilcoxon rank-sum test, the Friedman test, and the Nemenyi test. The results indicate that the BWSMA significantly outperforms these compared algorithms. In the comparison with the SMA variants, the BWSMA obtained average rankings of 1.414, 1.138, 1.069, and 1.414. In comparison with other improved algorithms, the BWSMA obtained average rankings of 2.583 and 1.833. Finally, the applicability of the BWSMA is further validated through two structural optimization problems. In conclusion, the proposed BWSMA is a promising algorithm with excellent search accuracy and robustness. Full article

This study presents a graph knowledge-enhanced iterated greedy algorithm that incorporates dual directional decoding strategies, disjunctive graphs, neighborhood structures, and a rapid evaluation method to demonstrate its superior performance for the hybrid flowshop scheduling problem (HFSP). The proposed algorithm addresses the trade-off between the finite solution space corresponding to solution representation and the search space for the optimal solution, as well as constructs a decision mechanism to determine which search operator should be used in different search stages to minimize the occurrence of futile searching and the low computational efficiency caused by individuals conducting unordered neighborhood searches. The algorithm employs dual decoding with a novel disturbance operation to generate initial solutions and expand the search space. The derivation of the critical path and the design of neighborhood structures based on it provide a clear direction for identifying and prioritizing operations that have a significant impact on the objective. The use of a disjunctive graph provides a clear depiction of the detailed changes in the job sequence both before and after the neighborhood searches, providing a comprehensive view of the operational sequence transformations. By integrating the rapid evaluation technique, it becomes feasible to identify promising regions within a constrained timeframe. The numerical evaluation with well-known benchmarks verifies that the performance of the graph knowledge-enhanced algorithm is superior to that of a prior algorithm, and seeks new best solutions for 183 hard instances. Full article

Deploying thousands of sensors across remote and challenging environments—such as the Amazon rainforest, Californian wilderness, or Australian bushlands—is a critical yet complex task for forest fire monitoring, while our backyard emulation confirmed the feasibility of small-scale deployment as a proof of concept, large-scale scenarios demand a scalable, efficient, and fault-tolerant network design. This paper proposes a Hierarchical Wireless Sensor Network (HWSN) deployment strategy with adaptive head node selection to maximize area coverage and energy efficiency. The network architecture follows a three-level hierarchy as follows: The first level incorporates cells of individual sensor nodes that connect to dynamically assigned cell heads. The second level involves the aggregated clusters of such cell heads, each with an assigned cluster head. Finally, dividing all cluster heads into regions, each with a region head, directly reports all the collected information from the forest floor to a central control sink room for decision making analysis. Unlike traditional centralized or uniformly distributed models, our adaptive approach leverages a greedy coverage maximization algorithm to dynamically select head nodes that contribute to the best forest sensed data coverage at each level. Through extensive simulations, the adaptive model achieved over 96.26% coverage, using significantly fewer nodes, while reducing node transmission distances and energy consumption. This facilitates the real-world deployment of our HWSN model in large-scale, remote forest regions, with very promising performance. Full article

The orthogonality of transmitted waveforms is an important factor affecting the performance of MIMO radar systems. The orthogonal coded signal is a commonly adopted waveform in MIMO radar, and its orthogonality depends on the used orthogonal discrete code sequence set (ODCSs). Among existing optimization algorithms for ODCSs, the results designed by the greedy code search-based memetic algorithm (MA-GCS) have exhibited the best autocorrelation and cross-correlation properties observed so far. Based on MA-GCS, we propose a novel hybrid algorithm called the memetic algorithm with iterative greedy code search (MA-IGCS). Extensions involve replacing the greedy code search used in MA-GCS with a more efficient approach, iterative greedy code search. Furthermore, we propose an “individual uniqueness strategy” and incorporate it into our algorithm to preserve population diversity throughout iteration, thereby preventing premature stagnation and ensuring the continued pursuit of feasible solutions. Finally, the design results of our algorithm are compared with the MA-GCS. Experimental results demonstrate that the MA-IGCS exhibits superior search capability and generates more favorable design results than the MA-GCS. Full article

A vehicle routing problem in timber logistics incorporating a comprehensive carbon emission reduction strategy is proposed. Carbon emission reduction based on an optimization model is established to minimize the total transportation cost while reducing carbon emissions and empty-loading mileage. To solve the problem efficiently, a hybrid algorithm that combines a greedy algorithm with a genetic algorithm featuring adaptive and elimination mechanisms is developed. The hybrid algorithm is featured with adaptive crossover and mutation probabilities and eliminates unsuitable individuals with elimination mechanisms, which improves the searching efficiency and quality of the optimal solution. Numerical experiments are conducted to verify the feasibility of the proposed methods. The results demonstrate that the hybrid algorithm reduces the total mileage travelled by 17.26% and the carbon emissions during empty-loading by about 38.71%. Based on the optimization results, it is concluded that reasonable route planning can provide a solid support to improve the economics, timeliness, and environmental sustainability of the timber logistics, which is conducive to realizing a sustainable forestry supply chain. Full article

Percolation theory has been widely employed in network systems as an effective tool to analyze phase transitions from functional to nonfunctional states. In this paper, we analyze the propagation of congestion on transportation networks and its influence on origin–destination (OD) pairs using the percolation process. This approach allows us to identify the most critical links within the network that, when disrupted due to congestion, significantly impact overall network performance. Understanding the role of these critical links is essential for developing strategies to mitigate congestion effects and enhance network resilience. Building on this analysis, we propose two methods to adjust the capacities of these critical links. First, we introduce a greedy method that incrementally adjusts the capacities based on their individual impact on network connectivity and traffic flow. Second, we employ a Particle Swarm Optimization (PSO) method to strategically increase the capacities of certain critical links, considering the network as a whole. These capacity adjustments are designed to enhance the network’s resilience by ensuring it remains functional even under conditions of high demand and congestion. By preventing the propagation of congestion through strategic capacity enhancements, the transportation network can maintain connectivity between OD pairs, reduce travel times, and improve overall efficiency. Our approach provides a systematic method for improving the robustness of transportation networks against congestion propagation. The results demonstrate that both the greedy method and the PSO method effectively enhance network performance, with the PSO method showing superior results in optimizing capacity allocations. This research is crucial for maintaining efficient and reliable mobility in urban areas, where congestion is a persistent challenge, and offers valuable insights for transportation planners and policymakers aiming to design more resilient transportation infrastructures. Full article

Research on helicopter dispatching has received considerable attention, particularly in relation to post-disaster rescue operations. The survival chances of individuals trapped in emergency situations decrease as time passes, making timely helicopter dispatch crucial for successful rescue missions. Therefore, this study investigates a collaborative helicopter rescue routing problem with time window and limited survival time constraints, solving it using an improved iterative greedy (IIG) algorithm. In the proposed algorithm, a heuristic initialization strategy is designed to generate an efficient and feasible initial solution. Then, a feasible-first destruction-construction strategy is applied to enhance the algorithm’s exploration ability. Next, a problem-specific local search strategy is developed to improve the algorithm’s local search effectiveness. In addition, the simulated annealing (SA) method is integrated as an acceptance criterion to avoid the algorithm from getting trapped in local optima. Finally, to evaluate the efficacy of the proposed IIG, 56 instances were generated based on Solomon instances and used for simulation tests. A comparative analysis was conducted against six efficient algorithms from the existing studies. The experimental results demonstrate that the proposed algorithm performs well in solving the post-disaster rescue helicopter routing problem. Full article

Adaptive spiral flight and multi-strategy fusion are the foundations of a new FOX optimization algorithm that aims to address the drawbacks of the original method, including weak starting individual ergodicity, low diversity, and an easy way to slip into local optimum. In order to enhance the population, inertial weight is added along with Levy flight and variable spiral strategy once the population is initialized using a tent chaotic map. To begin the process of implementing the method, the fox population position is initialized using the created Tent chaotic map in order to provide more ergodic and varied individual beginning locations. To improve the quality of the solution, the inertial weight is added in the second place. The fox random walk mode is then updated using a variable spiral position updating approach. Subsequently, the algorithm’s global and local searches are balanced, and the Levy flying method and greedy approach are incorporated to update the fox location. The enhanced FOX optimization technique is then thoroughly contrasted with various swarm intelligence algorithms using engineering application optimization issues and the CEC2017 benchmark test functions. According to the simulation findings, there have been notable advancements in the convergence speed, accuracy, and stability, as well as the jumping out of the local optimum, of the upgraded FOX optimization algorithm. Full article

As a product of modern development, logistics plays a significant role in economic growth with its advantages of integrated management, unified operations, and speed. With the rapid advancement of technology and economy, traditional manual storage and retrieval methods can no longer meet industry demands. Achieving efficient storage and retrieval of goods on densely packed, symmetrically shaped logistics shelves has become a critical issue that needs urgent resolution. The brain storm optimization (BSO) algorithm, introduced in 2010, has found extensive applications across various fields. This paper presents a modified BSO algorithm (MBSO) aimed at addressing the scheduling challenges of double-end automated storage and retrieval systems (DE-AS/RSs). Traditional AS/RSs suffer from slow scheduling efficiency and the current heuristic algorithms exhibit low accuracy. To overcome these limitations, we propose a new scheduling strategy for the stacker to select I/O stations in DE-AS/RSs. The MBSO incorporates two key enhancements to the basic BSO algorithm. First, it employs an objective space clustering method in place of the standard k-means clustering to achieve more accurate solutions for AS/RS scheduling problems. Second, it utilizes a mutation operation based on a greedy strategy and an improved crossover operation for updating individuals. Extensive comparisons were made between the well-known heuristic algorithms NIGA and BSO in several specific enterprise warehouse scenarios. The experimental results show that the MBSO has significant accuracy, optimization speed, and robustness in solving scheduling of AS/RSs. Full article

The number of real-time dynamic satellite observation missions has been rapidly increasing recently, while little attention has been paid to the dynamic mission-scheduling problem. It is crucial to reduce perturbations to the initial scheduling plan for the dynamic mission-scheduling as the perturbations have a significant impact on the stability of the Earth observation satellites (EOSs). In this paper, we focus on the EOS dynamic mission-scheduling problem, where the observation profit and perturbation are considered simultaneously. A multi-objective dynamic mission-scheduling mathematical model is first formulated. Then, we propose a multi-objective dynamic mission-scheduling algorithm (MODMSA) based on the improved Strength Pareto Evolutionary Algorithm (SPEA2). In the MODMSA, a novel two-stage individual representation, a minimum perturbation random initialization, multi-point crossover, and greedy mutation are designed to expand the search scope and improve the search efficiency. In addition, a profit-oriented local search algorithm is introduced into the SPEA2 to improve the convergence speed. Furthermore, an adaptive perturbation control strategy is adopted to improve the diversity of non−dominated solutions. Extensive experiments are conducted to evaluate the performance of the MODMSA. The simulation results show that the MODMSA outperforms other comparison algorithms in terms of solution quality and diversity, which demonstrates that the MODMSA is promising for practical EOS systems. Full article

To improve the performance of the sparrow search algorithm in solving complex optimization problems, this study proposes a novel variant called the Improved Beetle Antennae Search-Based Sparrow Search Algorithm (IBSSA). A new elite dynamic opposite learning strategy is proposed in the population initialization stage to enhance population diversity. In the update stage of the discoverer, a staged inertia weight guidance mechanism is used to improve the update formula of the discoverer, promote the information exchange between individuals, and improve the algorithm’s ability to optimize on a global level. After the follower’s position is updated, the logarithmic spiral opposition-based learning strategy is introduced to disturb the initial position of the individual in the beetle antennae search algorithm to obtain a more purposeful solution. To address the issue of decreased diversity and susceptibility to local optima in the sparrow population during later stages, the improved beetle antennae search algorithm and sparrow search algorithm are combined using a greedy strategy. This integration aims to improve convergence accuracy. On 20 benchmark test functions and the CEC2017 Test suite, IBSSA performed better than other advanced algorithms. Moreover, six engineering optimization problems were used to demonstrate the improved algorithm’s effectiveness and feasibility. Full article

This paper proposes a one-to-one trajectory synthetization method with stable long-term individual mobility behavior based on a generalizable area embedding. Previous methods concentrate on producing highly detailed data on short-term and restricted areas for, e.g., autonomous driving scenarios. Another possibility consists of city-wide and beyond scales that can be used to predict general traffic flows. The now-presented approach takes the tracked mobility behavior of individuals and creates coherent synthetic mobility data. These generated data reflect the person’s long-term mobility behavior, guaranteeing location persistency and sound embedding within the point-of-interest structure of the observed area. After an analysis and clustering step of the original data, the area is distributed into a geospatial grid structure (H3 is used here). The neighborhood relationships between the grids are interpreted as a graph. A feed-forward autoencoder and a graph encoding–decoding network generate a latent space representation of the area. The original clustered data are associated with their respective H3 grids. With a greedy algorithm approach and concerning privacy strategies, new combinations of grids are generated as top-level patterns for individual mobility behavior. Based on the original data, concrete locations within the new grids are found and connected to ways. The goal is to generate a dataset that shows equivalence in aggregated characteristics and distances in comparison with the original data. The described method is applied to a sample of 120 from a study with 1000 participants whose mobility data were generated in the city of Munich in Germany. The results show the applicability of the approach in generating synthetic data, enabling further research on individual mobility behavior and patterns. The result comprises a sharable dataset on the same abstraction level as the input data, which can be beneficial for different applications, particularly for machine learning. Full article