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However, in more complex problems, MOPSO faces the challenges of weak global search ability and easy-to-fall-into local optimality. To address these challenges and obtain better solutions, people have proposed many variants. In this study, a density-guided and adaptive update strategy for multi-objective particle swarm optimization (DAMOPSO) is proposed. First, an adaptive grid is used to determine the mutation particles and guides. Then, the Cauchy mutation operator is performed for the poorly distributed particles to expand the search space of the population. Additionally, the strategy of non-dominated sorting and hyper-region density are devised for maintaining external archives, which contribute to the uniform distribution of optimal solutions. Finally, an adaptive detection strategy based on the adjustment coefficient and conversion efficiency is designed to update the flight parameters. These approaches not only speed up the convergence of algorithms, but also balance exploitation and exploration more effectively. The proposed algorithm is compared with several representative multi-objective optimization algorithms on 22 benchmark functions; meanwhile, statistical tests, ablation experiments, analysis of stability, and complexity are also performed. The experimental results demonstrate DAMOPSO is more competitive than other comparison algorithms.<\/jats:p>","DOI":"10.1093\/jcde\/qwae081","type":"journal-article","created":{"date-parts":[[2024,9,15]],"date-time":"2024-09-15T04:36:28Z","timestamp":1726374988000},"page":"222-258","source":"Crossref","is-referenced-by-count":8,"title":["Density-guided and adaptive update strategy for multi-objective particle swarm optimization"],"prefix":"10.1093","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-2636-9976","authenticated-orcid":false,"given":"Xiaoyan","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics, Guizhou University , Guiyang, Guizhou 550025 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanmin","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mathematics, Zunyi Normal College , Zunyi, Guizhou 563002 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Song","sequence":"additional","affiliation":[{"name":"School of Data Science and Information Engineering, Guizhou Minzu University , Guiyang, Guizhou 550025 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yansong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Guizhou University , Guiyang, Guizhou 550025 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Mathematics, Zunyi Normal College , Zunyi, Guizhou 563002 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingtao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mathematics, Zunyi Normal College , Zunyi, Guizhou 563002 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2024,9,14]]},"reference":[{"key":"2024101613180167500_bib1","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1109\/TEVC.2014.2339823","article-title":"A decomposition-based evolutionary algorithm for many objective optimizations","volume":"19","author":"Asafuddoula","year":"2014","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"2024101613180167500_bib2","doi-asserted-by":"publisher","first-page":"108192","DOI":"10.1016\/j.asoc.2021.108192","article-title":"Patrol robot path planning in nuclear power plant using an interval multi-objective particle swarm optimization algorithm","volume-title":"Applied Soft Computing","author":"Chen","year":"2022"},{"key":"2024101613180167500_bib3","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1007\/s10710-005-6164-x","article-title":"Solving multiobjective optimization problems using an artificial immune system","volume":"6","author":"Coello","year":"2005","journal-title":"Genetic Programming and Evolvable Machines"},{"key":"2024101613180167500_bib5","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.1109\/CEC.2002.1004388","article-title":"MOPSO: a proposal for multiple objective particle swarm optimization","volume-title":"Proceedings of the 2002 Congress on Evolutionary Computation, CEC'02 (Cat. 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