BMJAYA: A parameter-free multi-strategy enhanced JAYA algorithm for global optimization and XGBoost hyperparameter optimization

BMJAYA: A parameter-free multi-strategy enhanced JAYA algorithm for global optimization and XGBoost hyperparameter optimization | 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 BMJAYA: A parameter-free multi-strategy enhanced JAYA algorithm for global optimization and XGBoost hyperparameter optimization Xianzhong Jian, Hui Chen, Jinyin Xiao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9107120/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Metaheuristic optimization algorithms are widely used for solving complex engineering optimization problems, particularly those involving high dimensionality, nonlinear coupling, and multimodal search landscapes. Nevertheless, many existing algorithms still face challenges such as rapid diversity loss, premature convergence, and limited robustness in complicated search spaces. In this paper, an enhanced parameter-free JAYA algorithm, termed BMJAYA, is proposed to overcome these limitations while preserving the simplicity of the original framework. The proposed method integrates three complementary strategies: a conditionally triggered Best--Mean--Random (BMR) enhancement mechanism for stagnation repair, a multipopulation cooperative evolution framework for diversity preservation, and a global elite sharing mechanism for accelerating information exchange among subpopulations. The effectiveness of BMJAYA is evaluated on the CEC-2022 benchmark suite and further examined in an engineering application of XGBoost hyperparameter optimization for corrosion behavior prediction. Comparative results show that BMJAYA achieves better convergence accuracy, stronger robustness, and more competitive overall performance than PSO, GWO, DE, WSO, and the original JAYA algorithm. In addition, the BMJAYA-XGBoost model yields improved predictive performance on Bode, Nyquist, and potentiodynamic polarization datasets. These results demonstrate that BMJAYA provides an effective parameter-free optimization framework for complex engineering optimization and machine-learning-based model tuning tasks. JAYA algorithm parameter-free optimization multipopulation evolution XGBoost hyperparameter tuning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Reviewers agreed at journal 28 Apr, 2026 Reviewers agreed at journal 26 Apr, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers invited by journal 22 Apr, 2026 Editor assigned by journal 22 Apr, 2026 Submission checks completed at journal 16 Mar, 2026 First submitted to journal 12 Mar, 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. 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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-9107120","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":630827985,"identity":"ee4bfa98-2f79-409e-a083-89649e77292e","order_by":0,"name":"Xianzhong Jian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYBACxmYwZQPlshGvJQ2ImYnUAgWHSdDC3M787DHPn/OJ8/vPH2D4UHaYgX92AyGHsZkbzuC5nbjhRjID44xzhxkk7hwgpIXBTOKDBFCLBDMDM2/bYQYDiQRCWti/SSQYnAM67DAD81/itPAAbUk4kNhwIJmBmZFILWWSMw4kGwP9YnCw51w6j8QNAloM+49vk+b5Yyc7v//gwwc/yqzl+GcQ0tKAxDkAxDz41QOBPEEVo2AUjIJRMAoAEtw/nXy0JK8AAAAASUVORK5CYII=","orcid":"","institution":"University of Shanghai for Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Xianzhong","middleName":"","lastName":"Jian","suffix":""},{"id":630827987,"identity":"49171653-a40a-4449-8654-f87916d3fcee","order_by":1,"name":"Hui Chen","email":"","orcid":"","institution":"University of Shanghai for Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Chen","suffix":""},{"id":630827990,"identity":"9e8e1256-4546-434b-ac78-f225c266d772","order_by":2,"name":"Jinyin Xiao","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jinyin","middleName":"","lastName":"Xiao","suffix":""}],"badges":[],"createdAt":"2026-03-12 16:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9107120/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9107120/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108491233,"identity":"31fe3ead-d309-4b59-be6d-2243b853af43","added_by":"auto","created_at":"2026-05-05 09:53:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1492342,"visible":true,"origin":"","legend":"","description":"","filename":"BMJAYApaper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9107120/v1_covered_c44df523-2e03-4785-baa8-9214154e2d69.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"BMJAYA: A parameter-free multi-strategy enhanced JAYA algorithm for global optimization and XGBoost hyperparameter optimization","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"the-journal-of-supercomputing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Journal of Supercomputing](https://www.springer.com/journal/11227)","snPcode":"11227","submissionUrl":"https://submission.nature.com/new-submission/11227/3","title":"The Journal of Supercomputing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"JAYA algorithm, parameter-free optimization, multipopulation evolution, XGBoost hyperparameter tuning","lastPublishedDoi":"10.21203/rs.3.rs-9107120/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9107120/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMetaheuristic optimization algorithms are widely used for solving complex engineering optimization problems, particularly those involving high dimensionality, nonlinear coupling, and multimodal search landscapes. 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Comparative results show that BMJAYA achieves better convergence accuracy, stronger robustness, and more competitive overall performance than PSO, GWO, DE, WSO, and the original JAYA algorithm. In addition, the BMJAYA-XGBoost model yields improved predictive performance on Bode, Nyquist, and potentiodynamic polarization datasets. 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