A distributed multi-objective optimization algorithm with time-varying priorities for multi-agent systems

A distributed multi-objective optimization algorithm with time-varying priorities for multi-agent systems | 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 A distributed multi-objective optimization algorithm with time-varying priorities for multi-agent systems Shokoufeh Naderi, Maude Blondin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7292808/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 Real-world multi-agent systems often operate in dynamic environments where the importance of tasks evolves over time. Traditional multi-objective optimization methods typically assume fixed or uniformly weighted objectives, limiting the system’s adaptability and responsiveness. This paper presents a new distributed multi-objective optimization (MOO) algorithm designed for multi-agent systems (MASs) with time-varying priorities. Building upon previous foundational work, the proposed algorithm addresses the dynamic nature of real-world applications by allowing agents to adjust their priorities over time. This flexibility enhances the system’s adaptability and responsiveness, which is crucial for emergency responses and complex robotic coordination tasks. Mathematical proofs of convergence and performance bounds are provided, confirming the algorithm’s effectiveness in managing conflicting objectives in dynamic environments. The algorithm’s performance is demonstrated through simulations involving small and large-scale agent networks. To further demonstrate the algorithm’s applicability in a simulated environment, it has been employed to generate the reference trajectory for a swarm of autonomous ground robots in a simulated rendezvous problem. Additionally, a Sugeno-type fuzzy controller has been designed to enhance the robots’ trajectory-following capabilities within the simulation. The results of the simulation of the swarm of robots for the application of the rendezvous problem with the fuzzy controller are presented proving the performance of both the algorithm and the controller. Distributed multi-objective optimization Multi-agent systems Consensus problem Time-varying priorities Fuzzy controller Rendezvous problem 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-7292808","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":500914824,"identity":"ed5e6bfa-ce5b-456a-9a5e-1c00f04b0a16","order_by":0,"name":"Shokoufeh Naderi","email":"data:image/png;base64,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","orcid":"","institution":"Université de Sherbrooke","correspondingAuthor":true,"prefix":"","firstName":"Shokoufeh","middleName":"","lastName":"Naderi","suffix":""},{"id":500914825,"identity":"464ed474-c0b4-4a95-b577-cfd00778193e","order_by":1,"name":"Maude Blondin","email":"","orcid":"","institution":"Université de Sherbrooke","correspondingAuthor":false,"prefix":"","firstName":"Maude","middleName":"","lastName":"Blondin","suffix":""}],"badges":[],"createdAt":"2025-08-04 15:38:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7292808/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7292808/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90031962,"identity":"b89455f3-63eb-455d-aa84-1a432512ae96","added_by":"auto","created_at":"2025-08-27 15:17:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1173703,"visible":true,"origin":"","legend":"","description":"","filename":"AAMASJournalPaper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7292808/v1_covered_59ade38c-1342-4c5c-8cc8-2826838fd231.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A distributed multi-objective optimization algorithm with time-varying priorities for multi-agent systems","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Distributed multi-objective optimization, Multi-agent systems, Consensus problem, Time-varying priorities, Fuzzy controller, Rendezvous problem","lastPublishedDoi":"10.21203/rs.3.rs-7292808/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7292808/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Real-world multi-agent systems often operate in dynamic environments where the importance of tasks evolves over time. 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