Multi-Objective Particle Swarm Algorithm Based on Density-Morphology Archive Maintenance and Dynamic Tracking Matrix Parameter Adjustment

Multi-Objective Particle Swarm Algorithm Based on Density-Morphology Archive Maintenance and Dynamic Tracking Matrix Parameter 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 Multi-Objective Particle Swarm Algorithm Based on Density-Morphology Archive Maintenance and Dynamic Tracking Matrix Parameter Adjustment Jiangyan Xu, Yanmin Liu, Siwan Chen, Yu Jiang, Jie Yang, Qian Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9409611/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Addressing the limitations of traditional multi-objective particle swarm optimization (MOPSO) in achieving coordinated optimization of convergence efficiency and solution set distribution, this paper proposes an improved algorithm (MCMOPSO). This algorithm integrates a dual-dimensional density-morphology profile maintenance method with a parameter adjustment mechanism based on a dynamic tracking matrix. Its innovations are concentrated in three aspects: constructing an external density-morphology profile maintenance scheme to achieve precise screening of non-dominated solutions; designing a dynamic tracking matrix recognition system to finely regulate particle trajectories; and establishing a percentile dominance framework to efficiently guide the population toward the Pareto optimal frontier (PF). Comparative experiments were conducted between this algorithm and 10 classical algorithms on three benchmark test problems (ZDT, UF, DTLZ). Performance was quantitatively analyzed using metrics such as hyper-volume (HV) and inverse generational distance (IGD). Results demonstrate that MCMOPSO exhibits superior convergence, distribution uniformity, and stability across most test problems, providing an efficient and reliable solution for complex multi-objective optimization. Density-based two-dimensional archive maintenance Dynamic tracking matrix parameter adjustment mechanism Percentile dominance framework Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 06 May, 2026 Reviews received at journal 29 Apr, 2026 Reviewers agreed at journal 26 Apr, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviewers invited by journal 24 Apr, 2026 Editor assigned by journal 19 Apr, 2026 Submission checks completed at journal 18 Apr, 2026 First submitted to journal 13 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. 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-9409611","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":634111615,"identity":"89c94be0-19ca-4985-9d82-3c5d39b49636","order_by":0,"name":"Jiangyan Xu","email":"","orcid":"","institution":"Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"Jiangyan","middleName":"","lastName":"Xu","suffix":""},{"id":634111616,"identity":"705788b7-42e1-4393-a620-e1301bcaee66","order_by":1,"name":"Yanmin 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