CLMOAS:Collaborative  Large-scale Multi-objective Optimization Algorithms with Adaptive Strategies

CLMOAS:Collaborative Large-scale Multi-objective Optimization Algorithms with Adaptive 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 CLMOAS:Collaborative Large-scale Multi-objective Optimization Algorithms with Adaptive Strategies Peng Wang, Yanxiu Fu, Huiping Yuan, Zhongyang Xiao, Chi Huang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6991211/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 the field of multi-objective evolutionary optimization, existing research has mostly focused on the scalability of the objective dimension, while insufficient attention has been paid to the scalability of the decision variable dimension. However, in many practical application scenarios, complex optimization problems with the co-existence of multi-objectives and large-scale decision variables are often faced. In consideration of this, this paper comes up with a novel large-scale multi-objective evolutionary optimization algorithm, the core idea of which is to classify the decision variables by clustering method, and on the basis of which the LMEA algorithm is improved, a new dominance relation is introduced, and the CLMOAS algorithm is constructed, aiming at effectively solving the dominance-resistance problem in the traditional dominance relation. The algorithm first utilizes the clustering technique to classify the decision variables into two categories: those related to convergence and those related to diversity. For these two categories of variables,Various optimization approaches have been developed to achieve targeted optimization. In the diversity-related strategy, a novel angle-based dominance relationship is introduced to reduce the dominance resistance encountered by the algorithm during the optimization process, so as to enhance the optimization efficiency and performance performance of the algorithm. To verify the performance advantages of the proposed algorithm, the paper performs comparative experiments between the LMEA algorithm and several other representative multi - objective evolutionary algorithms across multiple mainstream multi - objective optimization test sets. The experimental results show that the CLMOAS algorithm shows better performance than the original evolutionary algorithms on most of the test sets, verifying its effectiveness and superiority in solving multi-objective and large-scale decision variable problems. Physical sciences/Engineering Physical sciences/Mathematics and computing Evolutionary multi-objective optimization Many-objective optimization Large-scale optimization Clustering Dominance relationship 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-6991211","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":487881444,"identity":"79f5361b-c4e4-4887-8093-ad1630d69312","order_by":0,"name":"Peng Wang","email":"","orcid":"","institution":"Hunan First Normal University","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Wang","suffix":""},{"id":487881445,"identity":"b34e09aa-b9b5-42c2-9d0f-b2f4a51716fc","order_by":1,"name":"Yanxiu Fu","email":"","orcid":"","institution":"Hunan First Normal 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In the diversity-related strategy, a novel angle-based dominance relationship is introduced to reduce the dominance resistance encountered by the algorithm during the optimization process, so as to enhance the optimization efficiency and performance performance of the algorithm. To verify the performance advantages of the proposed algorithm, the paper performs comparative experiments between the LMEA algorithm and several other representative multi - objective evolutionary algorithms across multiple mainstream multi - objective optimization test sets. 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