Improving Many-Objective Optimization via An Adaptive Penalty Scheme and Jaya Operator in the MJaya/D Algorithm

Improving Many-Objective Optimization via An Adaptive Penalty Scheme and Jaya Operator in the MJaya/D Algorithm | 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 Improving Many-Objective Optimization via An Adaptive Penalty Scheme and Jaya Operator in the MJaya/D Algorithm Yue Zhang, Zhigang Yang, Zhijian Xiong, Yongqiang Wang, Ran Shen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4143705/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 Achieving an effective balance between solution convergence and diversity remains a key challenge when solving multi-objective optimization problems. However, the performance of existing algorithms continues to degrade on many-objective optimization problems. Additionally, the genetic operators commonly utilized in many-objective evolutionary algorithms require extensive tuning of control parameters, without which solution quality suffers. To address these limitations, several new techniques are proposed. First, an adaptive penalty mechanism that leverages population and weight vector distribution information to assign specialized penalty factors to each sub-problem is developed by us. Second, the Jaya optimization algorithm is extended by us to improve its efficacy in many-objective spaces. Finally, Levy mutation is incorporated to help escape local optima and enhance population diversity. The proposed MJaya/D algorithm is assessed on standard DTLZ benchmark problems to evaluate the performance improvements stemming from the Jaya operator and adaptive penalty scheme. Experiments demonstrate that MJaya/D obtains superior Pareto front approximations compared to current state-of-the-art techniques for several many-objective optimization formulations. Based on decomposition Angle penalty Jaya operator levy fight Many-objective optimization 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-4143705","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":282743314,"identity":"f4e315bb-66bf-4a1a-b9ed-9cbfbb5583ed","order_by":0,"name":"Yue Zhang","email":"","orcid":"","institution":"North China University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Zhang","suffix":""},{"id":282743315,"identity":"734a5055-7b86-4c77-a620-114ee6c3584f","order_by":1,"name":"Zhigang Yang","email":"","orcid":"","institution":"North China University of Science and 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