Multi-objective Particle Swarm Optimization Algorithm for Task Allocation and Archived Guided Mutation Strategies

Multi-objective Particle Swarm Optimization Algorithm for Task Allocation and Archived Guided Mutation Strategies | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Multi-objective Particle Swarm Optimization Algorithm for Task Allocation and Archived Guided Mutation Strategies Jianjie Chen, Yanmin Liu, Yi Luo, Aijia Ouyang, Jie Yang, Wuer Bai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5862633/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 May, 2025 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract In this paper, we propose a novel multi-objective particle swarm optimization algorithm with a task allocation and archive-guided mutation strategy (TAMOPSO), which effectively solves the problem of inefficient search in traditional algorithms by assigning different evolutionary tasks to particles with different characteristics. First, TAMOPSO divides multiple subpopulations according to the particle distribution status of each iteration of the population and designs a new task allocation mechanism to improve the evolutionary search efficiency. Second, TAMOPSO adopts an adaptive Lévy flight strategy according to the population growth rate, automatically increasing the global variation probability to expand the search range when the population converges and enhancing the local variation to conduct fine search when the population disperses to realize the dynamics of global and local variations. Finally, TAMOPSO measures the contribution of particles to the population optimization through the particle evolution contribution rate index and filters out valuable historical solutions for subsequent reuse to accelerate the convergence speed; in addition, TAMOPSO improves the individual optimal particle selection mechanism, changes the bias of the traditional algorithm, ensures that each particle has an equal opportunity, and enhances the fairness of the selection process. The fairness of the selection process is enhanced at the same time. The performance of TAMOPSO is compared with ten existing algorithms on 22 standard test problems, and the experimental results show that TAMOPSO outperforms the other algorithms in several standard test problems and has better performance in solving multi-objective problems. Physical sciences/Engineering Physical sciences/Mathematics and computing Multi-objective particle swarm optimization subpopulation partitioning task allocation Lévy flight strategy individual optimal selection Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 06 May, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Accepted 22 Apr, 2025 Reviews received at journal 20 Apr, 2025 Reviews received at journal 09 Apr, 2025 Reviewers agreed at journal 09 Apr, 2025 Reviewers agreed at journal 09 Apr, 2025 Reviewers invited by journal 09 Apr, 2025 Submission checks completed at journal 03 Apr, 2025 First submitted to journal 27 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5862633","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":440765890,"identity":"f6fa60df-d6a3-4333-b287-f5b5ed6f0275","order_by":0,"name":"Jianjie Chen","email":"","orcid":"","institution":"Guizhou Minzu University","correspondingAuthor":false,"prefix":"","firstName":"Jianjie","middleName":"","lastName":"Chen","suffix":""},{"id":440765891,"identity":"0d9b3066-e269-4f4b-85bc-82171c83211b","order_by":1,"name":"Yanmin 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