Adaptive complex network topology with fitness distance correlation framework for particle swarm optimization

International Journal of Intelligent SystemsVolume 37, Issue 8 p. 5217-5247 RESEARCH ARTICLE Adaptive complex network topology with fitness distance correlation framework for particle swarm optimization Wei Li, Wei Li orcid.org/0000-0001-8242-076X School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, Jiangxi, ChinaSearch for more papers by this authorBo Sun, Bo Sun School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, Jiangxi, ChinaSearch for more papers by this authorYing Huang, Corresponding Author Ying Huang [email protected] School of Mathematical and Computer Science, Gannan Normal University, Ganzhou, Jiangxi, China Correspondence Ying Huang, School of Mathematical and Computer Science, Gannan Normal University, Ganzhou, 341000 Jiangxi, China. Email: [email protected]Search for more papers by this authorSoroosh Mahmoodi, Soroosh Mahmoodi Soroosh Khorshid Iranian Co, Abyek Industrial Zone, Qazvin Province, IranSearch for more papers by this author Wei Li, Wei Li orcid.org/0000-0001-8242-076X School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, Jiangxi, ChinaSearch for more papers by this authorBo Sun, Bo Sun School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, Jiangxi, ChinaSearch for more papers by this authorYing Huang, Corresponding Author Ying Huang [email protected] School of Mathematical and Computer Science, Gannan Normal University, Ganzhou, Jiangxi, China Correspondence Ying Huang, School of Mathematical and Computer Science, Gannan Normal University, Ganzhou, 341000 Jiangxi, China. Email: [email protected]Search for more papers by this authorSoroosh Mahmoodi, Soroosh Mahmoodi Soroosh Khorshid Iranian Co, Abyek Industrial Zone, Qazvin Province, IranSearch for more papers by this author First published: 28 December 2021 https://doi.org/10.1002/int.22790Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Abstract The particle swarm optimization algorithm is an effective tool to solve various optimization problems due to the small number of parameters and the simple learning strategy. However, the updated strategy from the basic PSO mainly aims to learn the global optimal particles, and it often leads to premature convergence with poor solution accuracy. An adaptive complex network topology with a fitness distance correlation for the particle swarm optimization algorithm is proposed (CNAPSO). Using the CNAPSO algorithm, it is concluded that different network topologies have different degrees of dispersion in the process of particle swarm optimization search. Therefore, the adaptive strategy with the fitness distance correlation proposes to effectively balance the global exploration and local exploitation capabilities, which is the particle swarm adaptive network neighborhood topology. The neighborhood topology construction strategy with a complex network is used to construct the neighborhood topology for each particle. Therefore, the local optimal particles in the neighborhood participate in the search process of particle swarm optimization and eliminate the situation of only learning the global optimal particles. Moreover, it improves the solution accuracy of the particle swarm optimization algorithm. In addition, to avoid the particle swarm falling into premature convergence, this study introduces a random drift strategy to make the particles drift slightly and reduces the risk of premature convergence. The experimental results on twenty-four benchmark functions show that CNAPSO has great improvements in the accuracy of the solution and the speed of convergence compared with the six representative PSO algorithms. CONFLICT OF INTERESTS The authors declare that there are no conflict of interests. Citing Literature Volume37, Issue8August 2022Pages 5217-5247 RelatedInformation