Swarm and Evolutionary Computation最新文献_第8页

Experimental survey of L-SHADE and SHADE-based adaptive differential evolution algorithms L-SHADE和基于shade的自适应差分进化算法实验综述

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-02-01 Epub Date: 2026-01-27 DOI: 10.1016/j.swevo.2026.102286

Adam P. Piotrowski , Agnieszka E. Piotrowska , Jaroslaw J. Napiorkowski

{"title":"Experimental survey of L-SHADE and SHADE-based adaptive differential evolution algorithms","authors":"Adam P. Piotrowski , Agnieszka E. Piotrowska , Jaroslaw J. Napiorkowski","doi":"10.1016/j.swevo.2026.102286","DOIUrl":"10.1016/j.swevo.2026.102286","url":null,"abstract":"<div><div>Adaptive Differential Evolution (DE) methods are currently among the most efficient Evolutionary Algorithms. In the recent years different Success-History-Based Adaptive Differential Evolution algorithms (SHADE), often with linear population size reduction (commonly known as L-SHADE), have won numerous Competitions in Evolutionary Computation. Since 2014, the number and the variety of SHADE or L-SHADE-based algorithms flourished, encompassing novel operators and procedures. However, it is unclear which new SHADE/L-SHADE operators and procedures are the most successful, or efficient, for specific kinds of problems. After more than a decade of rapid development, some large-scale empirical tests are needed to select the best SHADE/L-SHADE-based algorithms for different purposes. This paper aims at a wide-scale inter-comparison between 32 SHADE/L-SHADE-based variants on large sets of various-dimensional benchmarks and on numerous real-world problems. We point at SHADE/L-SHADE-based algorithms that perform best for low-, or for high-dimensional problems. We determine variants that outperform others on simple problems, and those that perform best for more difficult tasks. Finally, we analyze which variants are best-suited for real-world applications, considering different computational budgets. Results indicate that much different SHADE/L-SHADE-based algorithms perform best for real-world problems than for numerical benchmark functions. Also, different algorithms may be recommended for higher, than for lower-dimensional problems, and other methods perform better for difficult problems than for unimodal ones. This discrepancy poses a challenge for choosing the appropriate algorithm for the specific application, and casts doubts on the classical way of justifying the introduction of novel variants.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"101 ","pages":"Article 102286"},"PeriodicalIF":8.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An efficient new method for traveling salesman problem using discrete evolutionary algorithm with special encoding and novel optimization strategy 采用特殊编码和新颖优化策略的离散进化算法，提出了求解旅行商问题的有效新方法

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-02-01 Epub Date: 2026-01-30 DOI: 10.1016/j.swevo.2026.102306

Yichao He , Guoxin Chen , Xizhao Wang , Haibin Ouyang , Manman Meng , Ju Chen

{"title":"An efficient new method for traveling salesman problem using discrete evolutionary algorithm with special encoding and novel optimization strategy","authors":"Yichao He , Guoxin Chen , Xizhao Wang , Haibin Ouyang , Manman Meng , Ju Chen","doi":"10.1016/j.swevo.2026.102306","DOIUrl":"10.1016/j.swevo.2026.102306","url":null,"abstract":"<div><div>In order to overcome the limitations of existing methods for solving traveling salesman problem (TSP) using evolutionary algorithms, this paper proposes a novel method for solving TSP based on group theory-based optimization algorithm (GTOA) and special encoding. Firstly, a new encoding method using special integer vector as individual encoding to represent TSP solution is proposed, which is proven to be completely equivalent to using permutation as TSP solution. It solves the bottleneck problem in which basic mathematical operations cannot be used to build evolution equations when using evolutionary algorithms to solve TSP. Secondly, by limiting the range of each component, an improved random mutation operator is proposed, which makes GTOA very suitable for solving TSP by using the new encoding method. Thirdly, a novel optimization method, fixed fragment exchange method (FFEM), is proposed to improve the structure of TSP solution. The combination of FFEM and 3-Opt can greatly improve the performance of the algorithm by simply optimizing the current best solution. Finally, a discrete evolutionary algorithm IGTOA is proposed based on GTOA to solve TSP. To verify the efficiency of IGTOA, it is used to solve 65 benchmark instances in TSPLIB. Based on the calculation results, the necessity of combining FFEM with 3-Opt is first analyzed. Then, by comparing IGTOA with 7 state-of-the-art evolutionary algorithms for solving TSP, it is shown that IGTOA has excellent ability to obtain the optimal solution, higher stability, and faster solving speed, and it is more competitive in solving TSP.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"101 ","pages":"Article 102306"},"PeriodicalIF":8.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic multi-objective optimization algorithm via historical collaborative strategy and interval prediction strategy 基于历史协同策略和区间预测策略的动态多目标优化算法

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-02-01 Epub Date: 2026-01-19 DOI: 10.1016/j.swevo.2026.102281

Junwei Ou , Yana Li , Yaru Hu , Jiankang Peng , Jinhua Zheng , Juan Zou , Shengxiang Yang

{"title":"Dynamic multi-objective optimization algorithm via historical collaborative strategy and interval prediction strategy","authors":"Junwei Ou , Yana Li , Yaru Hu , Jiankang Peng , Jinhua Zheng , Juan Zou , Shengxiang Yang","doi":"10.1016/j.swevo.2026.102281","DOIUrl":"10.1016/j.swevo.2026.102281","url":null,"abstract":"<div><div>Dynamic multi-objective optimization problems (DMOPs) involve scenarios where objective functions, decision variables, parameters, or other elements vary over time. An effective approach to address DMOPs is to integrate algorithms designed for static multi-objective optimization problems with dynamic response strategies. To improve the performance of these strategies in terms of both population diversity and convergence, this paper proposes a novel dynamic response strategy, the historical collaborative and interval prediction strategy (HCIPS). When confronted with environmental changes, we conduct a three-level population analysis: overall, historical, and individual. Firstly, the interval-based response strategy identifies interval partitioning of a population at time <span><math><mi>t</mi></math></span>, enabling global localization of the predicted population and effectively preserving diverse population information. Secondly, the history-based response strategy guides the population movement by selecting optimal solutions from historical populations. Thirdly, the individual-based response strategy predicts individual positions by tracking the movement of key points. This serves as a crucial complement to the history-based response strategy, compensating for its primary drawback: a lack of sufficient historical data in the early stages of evolution. Experimental results indicate that the HCIPS offers advantages in solving DMOPs compared to past state-of-the-art algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"101 ","pages":"Article 102281"},"PeriodicalIF":8.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cost-optimal analysis of Mn/Mn/1/K retrial queue with N-policy using ABC and QNM 基于ABC和QNM的n策略Mn/Mn/1/K重试队列成本最优分析

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-02-01 Epub Date: 2026-01-22 DOI: 10.1016/j.swevo.2026.102296

Vijaykumar Panchal, Sudeep Singh Sanga

{"title":"Cost-optimal analysis of Mn/Mn/1/K retrial queue with N-policy using ABC and QNM","authors":"Vijaykumar Panchal, Sudeep Singh Sanga","doi":"10.1016/j.swevo.2026.102296","DOIUrl":"10.1016/j.swevo.2026.102296","url":null,"abstract":"<div><div>This study investigates an <span><math><mrow><msub><mrow><mi>M</mi></mrow><mrow><mi>n</mi></mrow></msub><mo>/</mo><msub><mrow><mi>M</mi></mrow><mrow><mi>n</mi></mrow></msub><mo>/</mo><mn>1</mn><mo>/</mo><mi>K</mi></mrow></math></span> queueing model, incorporating two practical mechanisms: a retrial orbit and a service control <em>N</em>-policy. In this system, customers who find the server busy are directed to a retrial orbit, where they attempt to re-access the server at random intervals. Furthermore, the <em>N</em>-policy regulates the service process by activating it only when the queue size reaches a predetermined threshold <em>N</em> (1 <span><math><mo>≤</mo></math></span> <em>N</em> <span><math><mo>≤</mo></math></span> <em>K</em>). To analyze the model mathematically, Chapman–Kolmogorov (C-K) steady-state equations, based on the birth–death process, are formulated. These equations are subsequently solved using a recursive approach. Further, we derive the time-sharing model (TSM) and the machine repair problem (MRP) as special cases of the proposed model. We develop several key performance measures for the state-dependent model and analyze the impact of various input parameters on these performance measures for both the MRP and TSM. Additionally, a cost function is formulated with decision variables including threshold parameter <em>N</em> and service rate <span><math><mi>μ</mi></math></span>. For cost optimization, we use the direct search method (DSM), the quasi-Newton method (QNM), and the artificial bee colony (ABC) algorithm. The results are further validated using the Genetic algorithm (GA). Finally, the applicability of the proposed model in the context of TSM and MRP frameworks is demonstrated through real-world examples.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"101 ","pages":"Article 102296"},"PeriodicalIF":8.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A survey of features used for representing black-box single-objective continuous optimization 黑箱单目标连续优化的特征描述

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-02-01 Epub Date: 2026-01-14 DOI: 10.1016/j.swevo.2026.102288

Gjorgjina Cenikj , Ana Nikolikj , Gašper Petelin , Niki van Stein , Carola Doerr , Tome Eftimov

引用次数: 0

Composite indicator-guided infilling sampling for expensive multi-objective optimization 复合指标导向的昂贵多目标优化填充抽样

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-02-01 Epub Date: 2026-02-07 DOI: 10.1016/j.swevo.2026.102312

Huixiang Zhen , Xiaotong Li , Wenyin Gong , Xiangyun Hu

{"title":"Composite indicator-guided infilling sampling for expensive multi-objective optimization","authors":"Huixiang Zhen , Xiaotong Li , Wenyin Gong , Xiangyun Hu","doi":"10.1016/j.swevo.2026.102312","DOIUrl":"10.1016/j.swevo.2026.102312","url":null,"abstract":"<div><div>In computationally expensive multi-objective optimization, where the evaluation budget is severely limited, the selection of promising candidate solutions for costly fitness evaluations plays a critical role in accelerating convergence and enhancing algorithmic performance. Nevertheless, devising an optimization strategy that effectively balances convergence, diversity, and distribution remains a challenging task. To address this issue, this paper proposes a composite indicator-based evolutionary algorithm (CI-EMO) for expensive multi-objective optimization. During each generation of the optimization process, CI-EMO explores the solution space based on Gaussian Process model assisted NSGA-III, thereby generating a candidate population. Subsequently, a novel composite performance indicator is introduced to guide the selection of candidates for actual fitness evaluation. This indicator simultaneously accounts for convergence, diversity, and distribution, thereby enhancing the efficiency of identifying promising candidate solutions and significantly boosting algorithmic performance. The proposed composite indicator-based candidate selection strategy is straightforward to implement and computationally lightweight. Component analysis experiments validate the effectiveness of each constituent within the composite performance indicator. Comparative studies conducted on three benchmark test suites and real-world problems demonstrate that the proposed algorithm outperforms five state-of-the-art algorithms for expensive multi-objective optimization.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"101 ","pages":"Article 102312"},"PeriodicalIF":8.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147398165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed proximal primal–dual splitting for coupled constrained optimization over directed unbalanced networks 有向不平衡网络上耦合约束优化的分布近端原始对偶分裂

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-02-01 Epub Date: 2026-01-20 DOI: 10.1016/j.swevo.2026.102282

Run Tang , Huaqing Li , Dawen Xia , Liang Ran , Jun Li , Wei Zhu

{"title":"Distributed proximal primal–dual splitting for coupled constrained optimization over directed unbalanced networks","authors":"Run Tang , Huaqing Li , Dawen Xia , Liang Ran , Jun Li , Wei Zhu","doi":"10.1016/j.swevo.2026.102282","DOIUrl":"10.1016/j.swevo.2026.102282","url":null,"abstract":"<div><div>This paper investigates a distributed optimization problem over directed multi-agent networks in which each agent has access only to its own local cost function rgb]0.00,0.07,1.00subject to complex constraints, including coupled nonlinear inequality, equality, as well as a private constraint set. Departing from conventional column-stochastic matrix approaches that require explicit outdegree knowledge, a row-stochastic matrix-based method is proposed that inherently resolves graph imbalance without agent outdegree information. rgb]0.00,0.07,1.00Based on the dual decomposition framework and the prediction–correction mechanism, a novel distributed proximal primal–dual splitting method, named Dist_PPDSM, is developed for coupled constrained optimization problems. This method operates effectively over directed, unbalanced graph and establishes provable convergence across uncoordinated step sizes. The convergence and boundedness rgb]0.00,0.07,1.00of Dist_PPDSM are proved by mapping the algorithm to the framework of the maximal monotone operator. Rigorous convergence rate analysis under both general and structural convexity assumptions provides comprehensive performance guarantees across two distinct convexity paradigms. Finally, a simulation of an energy-management system demonstrates the effectiveness of the theoretical findings.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"101 ","pages":"Article 102282"},"PeriodicalIF":8.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An efficient binary ant colony evolutionary algorithm for feature selection 一种高效的二元蚁群特征选择进化算法

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-02-01 Epub Date: 2026-01-10 DOI: 10.1016/j.swevo.2025.102258

Peichen Xiong , Zhen Liu , Weiqing Xiong

{"title":"An efficient binary ant colony evolutionary algorithm for feature selection","authors":"Peichen Xiong , Zhen Liu , Weiqing Xiong","doi":"10.1016/j.swevo.2025.102258","DOIUrl":"10.1016/j.swevo.2025.102258","url":null,"abstract":"<div><div>Feature selection is an optimization problem of finding the optimal feature subset with high computational complexity and belongs to the NP-hard problem. Efficient direct solutions are typically unavailable, necessitating the design of algorithms to tackle such issues. To address this, we propose an efficient binary ant colony evolutionary algorithm to solve the feature selection problem, called EBACEA. Drawing on nucleic acid coding principles and the double helix structure of DNA, a binary network for ant traversal is developed, simplified into a one-dimensional chain by adding complement operators, and a corresponding pheromone-update operator was designed. Compared to the complete graph space, this algorithm requires low intelligence of individual agents, and the set of feasible solution nodes need not be explicitly recorded. Moreover, based on binary coding and inspired by DNA genetic principles, the algorithm incorporates three genetic operators: crossover, mutation and selection, to yield richer sets of solutions, can mitigate the risks of premature convergence and overcome the limitations of evolutionary computation in local search. To validate the performance of the proposed algorithm, we evaluated twelve public datasets and compared the results with those of eight swarm intelligence algorithms. The findings demonstrate that the proposed algorithm exhibits favorable execution time and achieves superior accuracy. Additionally, further comparative analysis confirms the proposed algorithm’s effectiveness.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"101 ","pages":"Article 102258"},"PeriodicalIF":8.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Auxiliary-wrapper guided and multi-criteria filter based evolutionary band selection algorithm for hyperspectral image classification 基于辅助包装和多准则滤波的高光谱图像分类进化带选择算法

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-02-01 Epub Date: 2026-02-09 DOI: 10.1016/j.swevo.2026.102326

Qijun Wang , Xiaowen Sun , Rui Chen , Guangyao Yan , Aibin Yan , Ye Tian , Xingyi Zhang

{"title":"Auxiliary-wrapper guided and multi-criteria filter based evolutionary band selection algorithm for hyperspectral image classification","authors":"Qijun Wang , Xiaowen Sun , Rui Chen , Guangyao Yan , Aibin Yan , Ye Tian , Xingyi Zhang","doi":"10.1016/j.swevo.2026.102326","DOIUrl":"10.1016/j.swevo.2026.102326","url":null,"abstract":"<div><div>The abundant bands contained in hyperspectral images (HSIs) may lead to significant challenges to subsequent data distribution and processing. Band selection (BS), as a dimensionality reduction technique, can efficiently mitigate the data volume of HSIs and retain the physical meaning in bands. Traditional filter based BS techniques often employ various metrics to assess the effectiveness of the selected band subset, rather than utilizing actual classification performance on the classifiers. However, these metrics often do not align with the actual classification performance. To tackle this problem, we propose the auxiliary-wrapper guided and multi-criteria filter based evolutionary BS algorithm for hyperspectral image classification, which integrates an auxiliary wrapper into the filter based BS process to select high-quality band subsets by combining filter metrics from a large number of unlabeled samples and real classification performance from a few labeled samples. Firstly, hyperspectral BS is formulated as a triple-objective optimization problem to evaluate the subsets of bands from multiple perspectives. Moreover, the auxiliary wrapper, where the classification performance of the selected bands is evaluated using only a few labeled samples and a basic classifier, is introduced to further guide the triple-objective optimization in the filter based BS. To keep the size of the selected bands stable in the evolutionary process, the leader-based learning strategy is designed, leveraging the transferring of the bands selected by the wrapper task to the filter task and further inside the filter task in a hierarchical manner. Experimental results on different standard HSI datasets show that the proposed WFBS method can achieve better band subsets compared with the existing unsupervised, semi-supervised and even deep learning based BS methods.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"101 ","pages":"Article 102326"},"PeriodicalIF":8.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147397635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A hybrid strategy enhanced stochastic fractal search algorithm for multi-robot path planning in dynamic obstacle environments 一种混合策略增强随机分形搜索算法用于动态障碍环境下的多机器人路径规划

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2026-02-01 Epub Date: 2026-02-09 DOI: 10.1016/j.swevo.2026.102316

Tianbao Liu, Zhe Feng

{"title":"A hybrid strategy enhanced stochastic fractal search algorithm for multi-robot path planning in dynamic obstacle environments","authors":"Tianbao Liu, Zhe Feng","doi":"10.1016/j.swevo.2026.102316","DOIUrl":"10.1016/j.swevo.2026.102316","url":null,"abstract":"<div><div>Robot path planning is essential for intelligent, autonomous operation, directly influencing efficiency, safety, and task performance. It also drives innovation and broadens the application of robotic technologies. Recently, meta-heuristic algorithms have shown encouraging performance in multi-robot path planning problems. However, the original Stochastic Fractal Search (SFS) algorithm struggles with low convergence accuracy and getting trapped in local optima for robotic path planning. To address these issues, this study proposes a Hybrid strategy Enhanced Stochastic Fractal Search (HESFS) algorithm. Firstly, a new population initialization strategy is proposed by integrating the Halton sequence, known for its high uniformity distribution characteristics, with a newly proposed random centroid opposition-based learning technique. Secondly, this study innovatively proposes an adaptive dynamic ranking mechanism (ADRM) based on individual fitness and the correlation information among neighboring individuals, along with a nonlinear weighting function. The parameters of this weighting function are obtained by solving a bi-objective optimization problem with constraints. Thirdly, a novel approximation strategy based on elliptic curves is proposed, which not only enhances the algorithm’s exploitation capability and solution accuracy but also effectively mitigates the risk of falling into local optima. Finally, this paper proposes an effective time-varying evolutionary boundary handling mechanism that enhances the convergence efficiency. To validate HESFS, the study first tested it on CEC2017 and CEC2005 benchmark functions, where it outperformed twelve classical algorithms, demonstrating significant superiority. Further, we applied HESFS to multi-robot path planning problem in complex environments with both static and dynamic obstacles. The experimental results demonstrate that HESFS achieves superior comprehensive performance.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"101 ","pages":"Article 102316"},"PeriodicalIF":8.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147397637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0