Research on Hybrid Strategy Particle Swarm Optimization Algorithm and Its Applications

Research on Hybrid Strategy Particle Swarm Optimization Algorithm and Its Applications | 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 Research on Hybrid Strategy Particle Swarm Optimization Algorithm and Its Applications Jicheng Yao, Xiaonan Luo, Fang Li, Jundi Dou, Hongtai Luo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4160937/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Oct, 2024 Read the published version in Scientific Reports → Version 1 posted 3 You are reading this latest preprint version Abstract The current original Particle Swarm Optimization (PSO) algorithm faces challenges such as susceptibility to local optima, slow search speed, and low precision in local search. To address this, a hybrid strategy PSO algorithm (HSPSO) is proposed in this paper. This algorithm enhances global search capability and search speed while improving local search precision through methods like adaptive weight adjustment, reverse learning strategy, Cauchy mutation mechanism, and Hook-Jeeves strategy. HSPSO demonstrates excellent performance in comprehensive comparisons on benchmark functions. Furthermore, when applied to feature selection experiments on the arrhythmia dataset, the constructed HSPSO-FS model performs remarkably, reaffirming the effectiveness of HSPSO. The proposed HSPSO algorithm exhibits significant efficacy in optimization problems, offering an effective approach to tackling complex issues. Particle swarm optimization algorithm Hybrid strategy Adaptive weight adjustment Reverse learning strategy Cauchy mutation mechanism Hook-Jeeves strategy Feature selection Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 22 Oct, 2024 Read the published version in Scientific Reports → Version 1 posted Editor invited by journal 10 Apr, 2024 Submission checks completed at journal 10 Apr, 2024 First submitted to journal 25 Mar, 2024 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-4160937","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":290255139,"identity":"ff9799dc-64c6-4025-a3dc-3549c22aecd9","order_by":0,"name":"Jicheng Yao","email":"","orcid":"","institution":"Guilin University of Electronic Technology","correspondingAuthor":false,"prefix":"","firstName":"Jicheng","middleName":"","lastName":"Yao","suffix":""},{"id":290255140,"identity":"5bd14f23-4f0c-40fd-a463-ce03ec071dfa","order_by":1,"name":"Xiaonan Luo","email":"","orcid":"","institution":"Guilin University of Electronic Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiaonan","middleName":"","lastName":"Luo","suffix":""},{"id":290255141,"identity":"2b360e88-afd1-4536-b801-b9682f40e573","order_by":2,"name":"Fang Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIie2OPQrCQBBGZxmITZJtIwHPsH0kuYqyoK2VdapUQduIl/AIK1ukkdgGIqh4AcFG/AHXYL9rJ7gPPj4G5jEDYLH8ID6Sg6r+Z3QMFAeRqRqpoKkCrSK/UTpILpPbNl6VcwHnqQS6TLWPYbiYNXy1kUCKSkKwEzqFitDLG85qDuhlElgw0F+5e3nF2f4E+DRUnNC9ipjVCEhMlchL+aC74WydV2M3qDUKpRIb9xEnfrk+Hq7TqEcLjdJCMhimqoWKa7D/5gGJ4abFYrH8Iy/i+DwMnGlAZgAAAABJRU5ErkJggg==","orcid":"","institution":"Guilin University of Electronic Technology","correspondingAuthor":true,"prefix":"","firstName":"Fang","middleName":"","lastName":"Li","suffix":""},{"id":290255142,"identity":"9d34b789-def3-4e39-ae5e-33701aa99e4b","order_by":3,"name":"Jundi Dou","email":"","orcid":"","institution":"Guilin University of Electronic Technology","correspondingAuthor":false,"prefix":"","firstName":"Jundi","middleName":"","lastName":"Dou","suffix":""},{"id":290255143,"identity":"b13037a9-23ea-41c6-950c-270ccbae2cf3","order_by":4,"name":"Hongtai Luo","email":"","orcid":"","institution":"Guilin University of Electronic Technology","correspondingAuthor":false,"prefix":"","firstName":"Hongtai","middleName":"","lastName":"Luo","suffix":""}],"badges":[],"createdAt":"2024-03-25 05:59:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4160937/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4160937/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-76010-y","type":"published","date":"2024-10-22T15:57:35+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":67682536,"identity":"00ae1de6-a69e-41cb-9033-87e608d5f30e","added_by":"auto","created_at":"2024-10-28 16:14:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":694022,"visible":true,"origin":"","legend":"","description":"","filename":"ResearchonHybridStrategyParticleSwarmOptimizationAlgorithmandItsApplicationsv1.0.2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4160937/v1_covered_f85ae81a-e99b-4a7c-af61-7b8244fb129d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on Hybrid Strategy Particle Swarm Optimization Algorithm and Its Applications","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":"Particle swarm optimization algorithm, Hybrid strategy, Adaptive weight adjustment, Reverse learning strategy, Cauchy mutation mechanism, Hook-Jeeves strategy, Feature selection","lastPublishedDoi":"10.21203/rs.3.rs-4160937/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4160937/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The current original Particle Swarm Optimization (PSO) algorithm faces challenges such as susceptibility to local optima, slow search speed, and low precision in local search. 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