Chao Li, Jun Sun, Li-Wei Li, Min Shan, Vasile Palade, Xiaojun Wu
{"title":"Virtual Position Guided Strategy for Particle Swarm Optimization Algorithms on Multimodal Problems.","authors":"Chao Li, Jun Sun, Li-Wei Li, Min Shan, Vasile Palade, Xiaojun Wu","doi":"10.1162/evco_a_00352","DOIUrl":null,"url":null,"abstract":"<p><p>Premature convergence is a thorny problem for particle swarm optimization (PSO) algorithms, especially on multimodal problems, where maintaining swarm diversity is crucial. However, most enhancement strategies for PSO, including the existing diversity-guided strategies, have not fully addressed this issue. This paper proposes the virtual position guided (VPG) strategy for PSO algorithms. The VPG strategy calculates diversity values for two different populations and establishes a diversity baseline. It then dynamically guides the algorithm to conduct different search behaviors, through three phases-divergence, normal, and acceleration-in each iteration, based on the relationships among these diversity values and the baseline. Collectively, these phases orchestrate different schemes to balance exploration and exploitation, collaboratively steering the algorithm away from local optima and towards enhanced solution quality. The introduction of \"virtual position\" caters to the strategy's adaptability across various PSO algorithms, ensuring the generality and effectiveness of the proposed VPG strategy. With a single hyperparameter and a recommended usual setup, VPG is easy to implement. The experimental results demonstrate that the VPG strategy is superior to several canonical and the state-of-the-art strategies for diversity guidance, and is effective in improving the search performance of most PSO algorithms on multimodal problems of various dimensionalities.</p>","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"427-458"},"PeriodicalIF":4.6000,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Evolutionary Computation","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1162/evco_a_00352","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Premature convergence is a thorny problem for particle swarm optimization (PSO) algorithms, especially on multimodal problems, where maintaining swarm diversity is crucial. However, most enhancement strategies for PSO, including the existing diversity-guided strategies, have not fully addressed this issue. This paper proposes the virtual position guided (VPG) strategy for PSO algorithms. The VPG strategy calculates diversity values for two different populations and establishes a diversity baseline. It then dynamically guides the algorithm to conduct different search behaviors, through three phases-divergence, normal, and acceleration-in each iteration, based on the relationships among these diversity values and the baseline. Collectively, these phases orchestrate different schemes to balance exploration and exploitation, collaboratively steering the algorithm away from local optima and towards enhanced solution quality. The introduction of "virtual position" caters to the strategy's adaptability across various PSO algorithms, ensuring the generality and effectiveness of the proposed VPG strategy. With a single hyperparameter and a recommended usual setup, VPG is easy to implement. The experimental results demonstrate that the VPG strategy is superior to several canonical and the state-of-the-art strategies for diversity guidance, and is effective in improving the search performance of most PSO algorithms on multimodal problems of various dimensionalities.
期刊介绍:
Evolutionary Computation is a leading journal in its field. It provides an international forum for facilitating and enhancing the exchange of information among researchers involved in both the theoretical and practical aspects of computational systems drawing their inspiration from nature, with particular emphasis on evolutionary models of computation such as genetic algorithms, evolutionary strategies, classifier systems, evolutionary programming, and genetic programming. It welcomes articles from related fields such as swarm intelligence (e.g. Ant Colony Optimization and Particle Swarm Optimization), and other nature-inspired computation paradigms (e.g. Artificial Immune Systems). As well as publishing articles describing theoretical and/or experimental work, the journal also welcomes application-focused papers describing breakthrough results in an application domain or methodological papers where the specificities of the real-world problem led to significant algorithmic improvements that could possibly be generalized to other areas.