An Adaptive Multi-Objective Particle Swarm Optimization Algorithm Based on Capacity-Driven Dual-Archive

An Adaptive Multi-Objective Particle Swarm Optimization Algorithm Based on Capacity-Driven Dual-Archive | 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 An Adaptive Multi-Objective Particle Swarm Optimization Algorithm Based on Capacity-Driven Dual-Archive Yuci Li, Yanmin Liu, Jing Zhang, Meiyi Liang, Siwan Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9378150/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 13 You are reading this latest preprint version Abstract Existing multi-objective particle swarm optimisation (MOPSO) algorithms often face bottlenecks when handling complex frontiers, such as gene loss caused by truncation of a single archive and search imbalance resulting from parameter stagnation. To address these challenges, this paper proposes a novel algorithm (CDAMOPSO) based on capacity-driven dual archiving and multi-source feedback coordination. The algorithm innovatively constructs an “overflow-mutation-refill” dual-repository mechanism, which transfers congested solutions overflowing from the main repository to the auxiliary repository for differential activation. This effectively reawakens dormant genes and breaks through population evolutionary stagnation. Concurrently, topological features such as population congestion and dispersion are extracted as feedback signals, and an adaptive closed-loop control law for parameters is designed to achieve dynamic, on-demand allocation of computational resources between global exploration and local exploitation. Furthermore, by introducing a sparse-grid-guided “vanguard-main force” hierarchical strategy, the algorithm utilises asymmetric collaboration among heterogeneous particles to accurately map complex Pareto fronts, including discontinuous and long-tail fronts. Comparative experiments based on 22 standard test sets and 10 mainstream algorithms confirm that CDAMOPSO achieves significant statistical advantages in both convergence accuracy (IGD) and comprehensive coverage (HV), demonstrating outstanding performance in solving complex multi-objective problems. multi-objective optimisation particle swarm optimisation dual-archive coordination adaptive control Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 15 May, 2026 Reviews received at journal 08 May, 2026 Reviews received at journal 04 May, 2026 Reviews received at journal 02 May, 2026 Reviewers agreed at journal 01 May, 2026 Reviewers agreed at journal 27 Apr, 2026 Reviews received at journal 27 Apr, 2026 Reviewers agreed at journal 27 Apr, 2026 Reviewers agreed at journal 27 Apr, 2026 Reviewers invited by journal 27 Apr, 2026 Editor assigned by journal 23 Apr, 2026 Submission checks completed at journal 23 Apr, 2026 First submitted to journal 10 Apr, 2026 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. 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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-9378150","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":634458862,"identity":"f2a1fad2-6c80-4b49-bdaf-9edd28368ab9","order_by":0,"name":"Yuci Li","email":"","orcid":"","institution":"Guizhou Minzu University","correspondingAuthor":false,"prefix":"","firstName":"Yuci","middleName":"","lastName":"Li","suffix":""},{"id":634458863,"identity":"fa7725bc-b6a9-4ad7-bc65-30b8ec51219e","order_by":1,"name":"Yanmin Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYLCCBIb/cmz8zQcOfPhBvBZmYz6JY4kHZ/YQbw9z4jyGHOPDHGxEqDU4fvbohoc72IzZGM58OMzAwyDPL3aAgJYzeWk3Es/wyLEx9244XGDBYDhzdgIBLQdyzG4ktkkAbTm74fAMHoYEg9uEtJx/A9JikNjGkPPgMA8bMVpugG1JAGlhIE6L5A2wLQeM2SSOGQADWYKwX/jO55jd/Nl2QE6+v/nxhw8/bOT5pQloUTiAypfArxwE5BsIqxkFo2AUjIKRDgA6ZEt3KkqP5QAAAABJRU5ErkJggg==","orcid":"","institution":"Zunyi Normal College","correspondingAuthor":true,"prefix":"","firstName":"Yanmin","middleName":"","lastName":"Liu","suffix":""},{"id":634458864,"identity":"d01c9740-ad1c-494d-875f-38fd32e2e47d","order_by":2,"name":"Jing Zhang","email":"","orcid":"","institution":"Guizhou Minzu University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Zhang","suffix":""},{"id":634458866,"identity":"f70b76ac-440e-49a6-8974-7c15743aa7e9","order_by":3,"name":"Meiyi Liang","email":"","orcid":"","institution":"Guizhou Minzu University","correspondingAuthor":false,"prefix":"","firstName":"Meiyi","middleName":"","lastName":"Liang","suffix":""},{"id":634458867,"identity":"3f72b079-a046-4720-9b1b-48929a08f3c7","order_by":4,"name":"Siwan Chen","email":"","orcid":"","institution":"Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"Siwan","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2026-04-10 10:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9378150/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9378150/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108606999,"identity":"59dea5fa-aa2e-427a-9013-019b662e14e3","added_by":"auto","created_at":"2026-05-06 12:26:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1637416,"visible":true,"origin":"","legend":"","description":"","filename":"AnAdaptiveMultiObjectiveParticleSwarmOptimizationAlgorithmBasedonCapacityDrivenDualArchive.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9378150/v1_covered_18197f4f-3a48-4f46-ac2b-8e7e5d9524da.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Adaptive Multi-Objective Particle Swarm Optimization Algorithm Based on Capacity-Driven Dual-Archive","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"international-journal-of-computational-intelligence-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [International Journal of Computational Intelligence Systems](https://link.springer.com/journal/44196)","snPcode":"44196","submissionUrl":"https://submission.springernature.com/new-submission/44196/3","title":"International Journal of Computational Intelligence Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"multi-objective optimisation, particle swarm optimisation, dual-archive coordination, adaptive control","lastPublishedDoi":"10.21203/rs.3.rs-9378150/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9378150/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eExisting multi-objective particle swarm optimisation (MOPSO) algorithms often face bottlenecks when handling complex frontiers, such as gene loss caused by truncation of a single archive and search imbalance resulting from parameter stagnation. To address these challenges, this paper proposes a novel algorithm (CDAMOPSO) based on capacity-driven dual archiving and multi-source feedback coordination. The algorithm innovatively constructs an \u0026ldquo;overflow-mutation-refill\u0026rdquo; dual-repository mechanism, which transfers congested solutions overflowing from the main repository to the auxiliary repository for differential activation. This effectively reawakens dormant genes and breaks through population evolutionary stagnation. Concurrently, topological features such as population congestion and dispersion are extracted as feedback signals, and an adaptive closed-loop control law for parameters is designed to achieve dynamic, on-demand allocation of computational resources between global exploration and local exploitation. 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