Dynamic Repulsion-Attraction Particle Swarm Optimization Based on Adaptive Adjustment

Dynamic Repulsion-Attraction Particle Swarm Optimization Based on Adaptive Adjustment | 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 Research Article Dynamic Repulsion-Attraction Particle Swarm Optimization Based on Adaptive Adjustment Wentao Ding, Wuyungerile Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6835984/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In recent years, the traditional Particle Swarm Optimization (PSO) algorithm has drawn increasing attention due to its deficiencies such as reduced population diversity and insufficient information sharing among particles. To overcome these limitations and enhance the algorithm's search capability, this paper proposes a new PSO variant called Dynamic Repulsion-Attraction PSO based on Adaptive Adjustment (DRA-PSO). The DRA-PSO introduces an inertia weight that varies with the number of iterations and learning factors that adjust according to the inertia weight to improve the adaptability of the algorithm. Furthermore, a repulsion-attraction strategy based on population diversity is designed to balance global and local search capabilities. Finally, a terminal elimination strategy is incorporated to establish a self-purification mechanism for population quality and sustain the activation of the exploration capability. To evaluate the performance of DRA-PSO, comparative experiments were conducted with four other improved PSO algorithms using nine benchmark test functions. The results demonstrate that DRA-PSO not only maintains population diversity but also achieves faster convergence and higher global optimization accuracy. Particle Swarm Optimization (PSO) Repulsion-Attraction Strategy Terminal Elimination Mechanism Population Diversity Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6835984","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":477802601,"identity":"96d2a97b-1525-4f8d-8460-c0cb8e7e2e85","order_by":0,"name":"Wentao Ding","email":"","orcid":"","institution":"Inner Mongolia University, Inner Mongolia Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Wentao","middleName":"","lastName":"Ding","suffix":""},{"id":477802602,"identity":"1e59065b-7679-4dac-b454-58e0b40a8b60","order_by":1,"name":"Wuyungerile Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYJCCAw8YbHjALB6itSQwpJGohSGB4TAD8Vrk3XsfHkioOC+jOyOB8cHbNgZ5c0JaDM8cNziQcOY2j9mNBGbDuW0MhjsbCGmZkcZwILENrIVNmreNIcHgACEt858Btfw7B9LC/psoLfISbEAtDQfAtjATpcWAB+iwhGPJPGZnHjZLzjknYbiBoC3tx5g/fKixszc7nnzww5syG3nCtiAUMDYACQkC6kG2NBBWMwpGwSgYBSMdAAArBUDaKO1qfQAAAABJRU5ErkJggg==","orcid":"","institution":"Inner Mongolia University","correspondingAuthor":true,"prefix":"","firstName":"Wuyungerile","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-06-06 10:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6835984/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6835984/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90668419,"identity":"545785b9-3eb3-41c2-87e4-798a3beb5f2c","added_by":"auto","created_at":"2025-09-05 13:08:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":713682,"visible":true,"origin":"","legend":"","description":"","filename":"DRAPSO2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6835984/v1_covered_5c105bed-d3ad-4864-a106-dd682c254436.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dynamic Repulsion-Attraction Particle Swarm Optimization Based on Adaptive Adjustment","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Particle Swarm Optimization (PSO), Repulsion-Attraction Strategy, Terminal Elimination Mechanism, Population Diversity","lastPublishedDoi":"10.21203/rs.3.rs-6835984/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6835984/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn recent years, the traditional Particle Swarm Optimization (PSO) algorithm has drawn increasing attention due to its deficiencies such as reduced population diversity and insufficient information sharing among particles. 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