A multi-strategy improved hunger games search algorithm

A multi-strategy improved hunger games search 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 Article A multi-strategy improved hunger games search algorithm Qiu yihui, Zhang Xinqiang, ruoyu Li, Li Dongyi, Xia Feihan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6092434/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract This paper proposes a Multi-strategy Improved Hunger Games Search (MHGS) algorithm to address the inherent limitations of the original HGS algorithm, including imbalanced exploration-exploitation capabilities, insufficient population diversity, and premature convergence. The main contributions feature four synergistic innovation mechanisms: (1) A phased position update framework dynamically coordinates global exploration and local exploitation through three distinct search phases; (2) An enhanced reproduction operator mimics biological reproductive patterns to maintain population diversity; (3) An adaptive boundary handling system redirects out-of-bounds individuals to promising regions, improving search efficiency; (4) An elite dynamic oppositional learning strategy with self-adjusting coefficients enhances local optima avoidance. The proposed mechanisms demonstrate synergistic effects: the phased update coordinates macro/micro-search patterns, while the reproduction operator and boundary handling jointly maintain solution diversity, complemented by oppositional learning's perturbation effects. Extensive evaluations on 23 benchmark functions, CEC2017 test suite, and two engineering designs reveal MHGS's superior performance, achieving 23.7% average accuracy improvement over seven state-of-the-art algorithms (Wilcoxon rank-sum test p < 0.05). Furthermore, the binary variant BMHGS_V3 with sigmoid transformation attains 92.3% average classification accuracy on ten UCI datasets for feature selection. The proposed algorithm establishes a novel framework for complex optimization, demonstrating both theoretical significance and practical value in computational intelligence. Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Information technology Hunger Games Search phased optimization dynamic oppositional learning feature selection metaheuristic algorithm Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 22 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 05 May, 2025 Reviews received at journal 02 May, 2025 Reviews received at journal 29 Apr, 2025 Reviewers agreed at journal 27 Apr, 2025 Reviewers agreed at journal 26 Apr, 2025 Reviewers invited by journal 26 Apr, 2025 Submission checks completed at journal 17 Apr, 2025 First submitted to journal 07 Apr, 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. 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-6092434","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":449551057,"identity":"74e73f31-60cb-4b9b-8edb-673822dd5ee3","order_by":0,"name":"Qiu yihui","email":"","orcid":"","institution":"Xiamen University of Technology，Xiamen","correspondingAuthor":false,"prefix":"","firstName":"Qiu","middleName":"","lastName":"yihui","suffix":""},{"id":449551058,"identity":"2e83987a-6595-4201-938d-9627b6ba29b9","order_by":1,"name":"Zhang Xinqiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIie2RsQ4BQRCGRy45zcYpRwivsCLZKCReQ7kXoiJUshIFIauRaD2GjpJIrpqorzxvQKcR3LVk0Sn2q+fLzPw/gMXyh7jedB+hqrGSN99HUo3Amy+4Uclg0ORVahfKK2ryiAJAOvaNShE6IjvUhxrspMidZg7wRu9sPgxIQEgOS413beWPXeDYXZsVRw9OK+WytDMJQn9b+EKBw6aCxJItoU/fbAEp8jeNLP6l7+vnL9gxJ+ZCS2BO80SBRGl8UuKQkSSLQ0ZJAUMis1JaJlXe63GVl6saFT9W+QL7bdxisVgsb3kAMElOH8Kw+vYAAAAASUVORK5CYII=","orcid":"","institution":"Xiamen University of Technology，Xiamen","correspondingAuthor":true,"prefix":"","firstName":"Zhang","middleName":"","lastName":"Xinqiang","suffix":""},{"id":449551059,"identity":"643fb1e2-514c-4e4f-a1d1-b72bc1086289","order_by":2,"name":"ruoyu Li","email":"","orcid":"","institution":"Xiamen University of Technology，Xiamen","correspondingAuthor":false,"prefix":"","firstName":"ruoyu","middleName":"","lastName":"Li","suffix":""},{"id":449551060,"identity":"2dfc9e49-677f-4671-b0dd-614a44656404","order_by":3,"name":"Li Dongyi","email":"","orcid":"","institution":"Xiamen University of Technology，Xiamen","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Dongyi","suffix":""},{"id":449551061,"identity":"5c770040-4600-41ff-8023-0b28f3bceca1","order_by":4,"name":"Xia Feihan","email":"","orcid":"","institution":"Xiamen University of Technology，Xiamen","correspondingAuthor":false,"prefix":"","firstName":"Xia","middleName":"","lastName":"Feihan","suffix":""}],"badges":[],"createdAt":"2025-02-24 01:38:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6092434/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6092434/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-16513-4","type":"published","date":"2025-08-22T16:29:56+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89847510,"identity":"794cc988-be57-4095-9451-24018bbb4f97","added_by":"auto","created_at":"2025-08-25 16:43:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1625372,"visible":true,"origin":"","legend":"","description":"","filename":"MHGS211.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6092434/v1_covered_47701238-e92b-4584-911d-e827f889ebde.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A multi-strategy improved hunger games search algorithm","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Hunger Games Search, phased optimization, dynamic oppositional learning, feature selection, metaheuristic algorithm","lastPublishedDoi":"10.21203/rs.3.rs-6092434/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6092434/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper proposes a Multi-strategy Improved Hunger Games Search (MHGS) algorithm to address the inherent limitations of the original HGS algorithm, including imbalanced exploration-exploitation capabilities, insufficient population diversity, and premature convergence. The main contributions feature four synergistic innovation mechanisms: (1) A phased position update framework dynamically coordinates global exploration and local exploitation through three distinct search phases; (2) An enhanced reproduction operator mimics biological reproductive patterns to maintain population diversity; (3) An adaptive boundary handling system redirects out-of-bounds individuals to promising regions, improving search efficiency; (4) An elite dynamic oppositional learning strategy with self-adjusting coefficients enhances local optima avoidance. The proposed mechanisms demonstrate synergistic effects: the phased update coordinates macro/micro-search patterns, while the reproduction operator and boundary handling jointly maintain solution diversity, complemented by oppositional learning's perturbation effects. 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