A Comprehensive Comparison of Constrained Multi-Objective Evolutionary Algorithms

A Comprehensive Comparison of Constrained Multi-Objective Evolutionary Algorithms | 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 Comprehensive Comparison of Constrained Multi-Objective Evolutionary Algorithms Mengjun Ming, Rui Wang, Xin Zhou, Tao Zhang, Hisao Ishibuchi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7653989/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 21 You are reading this latest preprint version Abstract In order to solve constrained multi-objective optimization problems (CMOPs), many algorithms have been proposed in the evolutionary computation research community in the past two decades. In general, the effectiveness of an algorithm on CMOPs is evaluated by artificial test problems. At present, comprehensive performance comparisons of constraint-handling techniques on various test problems remain scarce. In this study, we selected 12 representative algorithms that integrate with different constraint-handling techniques and compared their performance on existing test suites. Performance comparisons identify the strengths and weaknesses of different constraint-handling techniques on different types of CMOPs, and provide guidance on how to select/design constraint-handling techniques in specific scenarios. In addition, after a careful review of current artificial test problems, we found out that their design is inadequate to reflect some characteristics of real-world applications (e.g., the change in dimensionality of the Pareto front due to the inclusion of constraints, a large number of constraints, and diverse geometries of unconstrained Pareto fronts). Therefore, we point out the need to further develop test problems with these real-world characteristics. Constrained multi-objective optimization test suites constraint-handling techniques Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 25 Nov, 2025 Reviews received at journal 23 Nov, 2025 Reviews received at journal 20 Nov, 2025 Reviewers agreed at journal 13 Nov, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviews received at journal 10 Nov, 2025 Reviewers agreed at journal 10 Nov, 2025 Reviewers agreed at journal 08 Nov, 2025 Reviews received at journal 07 Nov, 2025 Reviews received at journal 05 Nov, 2025 Reviews received at journal 05 Nov, 2025 Reviewers agreed at journal 30 Oct, 2025 Reviewers agreed at journal 27 Oct, 2025 Reviewers agreed at journal 19 Oct, 2025 Reviewers agreed at journal 13 Oct, 2025 Reviewers agreed at journal 11 Oct, 2025 Reviewers invited by journal 11 Oct, 2025 Editor assigned by journal 03 Oct, 2025 Submission checks completed at journal 25 Sep, 2025 First submitted to journal 18 Sep, 2025 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. 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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-7653989","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":532951221,"identity":"9e4548fc-48cf-4464-aede-515430f11c59","order_by":0,"name":"Mengjun 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