Multi-strategy Integrated Grey Wolf Optimization Algorithm

Multi-strategy Integrated Grey Wolf Optimization 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 Multi-strategy Integrated Grey Wolf Optimization Algorithm Xiaozeng Xu, Xinyuan Gao, Bo Zeng, Liming Zou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6557735/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 The grey wolf optimizer (GWO) has gained significant recognition in metaheuristic research owing to its straightforward implementation and minimal parameter configuration requirements. Nevertheless, conventional GWO implementations exhibit limitations in addressing complex optimization challenges, particularly premature convergence tendencies and susceptibility to local optima entrapment. This investigation presents an enhanced multi-strategy framework incorporating chaotic initialization sequences, dual nonlinear convergence operators, adaptive weighting mechanisms, and elite preservation techniques, while synergistically integrating predation strategies from both Aquila Optimizer (AO) and Whale Optimization Algorithm (WOA). The methodological innovations unfold through three primary phases: (1) Chaotic logistic mapping generates diversified initial populations to prevent solution clustering; (2) An arctangent-based convergence operator dynamically regulates leadership parameters across exploration-exploitation phases; (3) Hybridized search patterns combine AO's vertical dive dynamics with WOA's spiral updating mechanisms. Population diversity maintenance is achieved through elite archival strategies that preserve superior solutions throughout iterations. Rigorous evaluation employing CEC2005's 23 benchmark functions reveals substantial improvements in convergence precision and computational efficiency compared with eight contemporary optimization algorithms. The algorithm is used for three real-world engineering problems, and the results show that the proposed algorithm is very effective in the unknown exploration space for challenging problems. Grey Wolf Optimization Algorithm Chaotic Mapping Dual Nonlinear Convergence Factors Multi-strategy Combination 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. 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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-6557735","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":496915033,"identity":"a2e3a96d-4bfe-466a-88dd-19059913b310","order_by":0,"name":"Xiaozeng Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYDACZgST8QEDwwEQw4BoLcwGDAnEaEECbBJEaTE4zvzw4Y+KO3YNEjlmFT9/3ElsYG/eJsFQcwenFslmNmMDiTPPkht4zpjd7El4ltjAc6xMguHYM5xa+JkZzCQM2w4nM7D3mN1mSDicCLJOgrHhMG73M7N/k0j8B9TCzGNWDNYi/wa/Fn6gSomDDYftQLYwQ2zhwa9Fspmn2LDh2OEENp5jxZI9ac+M23jSii0SjuHWYnD++MaHP2oO2/NLJG/88MPmjmw/++GNNz7U4NYCA4ltcN+BiASCGhgY7IlQMwpGwSgYBSMVAADhY1EV7lvpZAAAAABJRU5ErkJggg==","orcid":"","institution":"Chongqing Technology and Business University","correspondingAuthor":true,"prefix":"","firstName":"Xiaozeng","middleName":"","lastName":"Xu","suffix":""},{"id":496915034,"identity":"40391506-7468-4a4e-93e0-6b6523d6f5b8","order_by":1,"name":"Xinyuan Gao","email":"","orcid":"","institution":"Chongqing Technology and Business University","correspondingAuthor":false,"prefix":"","firstName":"Xinyuan","middleName":"","lastName":"Gao","suffix":""},{"id":496915036,"identity":"4f1aaab9-71a7-412d-8d53-372336f6ce24","order_by":2,"name":"Bo Zeng","email":"","orcid":"","institution":"Chongqing Technology and Business University","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Zeng","suffix":""},{"id":496915040,"identity":"47b1cc4b-a021-4814-a422-aede927844d8","order_by":3,"name":"Liming Zou","email":"","orcid":"","institution":"Chongqing Technology and Business University","correspondingAuthor":false,"prefix":"","firstName":"Liming","middleName":"","lastName":"Zou","suffix":""}],"badges":[],"createdAt":"2025-04-29 15:08:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6557735/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6557735/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92027076,"identity":"a8ec9c40-2092-4f9d-bea5-352b339414a0","added_by":"auto","created_at":"2025-09-23 19:08:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6447050,"visible":true,"origin":"","legend":"","description":"","filename":"MGWOTheJournalofSupercomputing.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6557735/v1_covered_c1a78b94-7c25-4181-9c8b-f0a457527425.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-strategy Integrated Grey Wolf Optimization Algorithm","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Grey Wolf Optimization Algorithm, Chaotic Mapping, Dual Nonlinear Convergence Factors, Multi-strategy Combination","lastPublishedDoi":"10.21203/rs.3.rs-6557735/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6557735/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The grey wolf optimizer (GWO) has gained significant recognition in metaheuristic research owing to its straightforward implementation and minimal parameter configuration requirements. Nevertheless, conventional GWO implementations exhibit limitations in addressing complex optimization challenges, particularly premature convergence tendencies and susceptibility to local optima entrapment. This investigation presents an enhanced multi-strategy framework incorporating chaotic initialization sequences, dual nonlinear convergence operators, adaptive weighting mechanisms, and elite preservation techniques, while synergistically integrating predation strategies from both Aquila Optimizer (AO) and Whale Optimization Algorithm (WOA). The methodological innovations unfold through three primary phases: (1) Chaotic logistic mapping generates diversified initial populations to prevent solution clustering; (2) An arctangent-based convergence operator dynamically regulates leadership parameters across exploration-exploitation phases; (3) Hybridized search patterns combine AO's vertical dive dynamics with WOA's spiral updating mechanisms. Population diversity maintenance is achieved through elite archival strategies that preserve superior solutions throughout iterations. Rigorous evaluation employing CEC2005's 23 benchmark functions reveals substantial improvements in convergence precision and computational efficiency compared with eight contemporary optimization algorithms. 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