Improvement of the whale optimization algorithm and its application to engineering design problems

Improvement of the whale optimization algorithm and its application to engineering design problems | 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 Improvement of the whale optimization algorithm and its application to engineering design problems Tu Binbin, Fynn Fei, Huo Yan, Wang Xiaotian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3825404/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 Aiming at the problems of insufficient global exploration ability, low convergence accuracy and slow speed of the standard whale optimization algorithm, the paper proposes a dimension-based neighborhood search strategy, which constructs a neighborhood for each search agent during iteration, and the search agents in this neighborhood can share the search information; considering that the motion of the search agent is a kind of jumping movement assuming successive jumps, which may cause the search agent to prematurely fall into local optimum, so adaptive weights are added to regulate the position update. The improved whale optimization algorithm (notated as: DWOA) is mainly used to solve global optimization and engineering design problems. DWOA and other excellent whale optimization algorithm improvement schemes are evaluated by 23 benchmark test functions and 5 engineering design problems, and the experimental results show that DWOA has strong competitiveness in terms of global exploration ability, local exploitation ability, convergence speed and convergence accuracy. Meanwhile, the improved algorithm has obvious advantages in solving engineering design problems, which also proves its effectiveness and applicability. whale optimization algorithm adaptive weighting neighbourhood search benchmarking functions engineering design problems 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-3825404","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":264785967,"identity":"0f6d852d-8bdd-48e1-a6f4-e07a1f8713f2","order_by":0,"name":"Tu Binbin","email":"","orcid":"","institution":"Shenyang University","correspondingAuthor":false,"prefix":"","firstName":"Tu","middleName":"","lastName":"Binbin","suffix":""},{"id":264785968,"identity":"d450c3a9-7327-4fb1-ab22-3d15f9f3a0b3","order_by":1,"name":"Fynn Fei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYBAC9gbGBjCDjZn54IMPQAYfAwMzXi08B6Ba+Njbkg1nMDBIsBHWAmXI8Zwxk+YhSgt7cwMzT80duzaJHGNjm4rDdWzszYcNGGpsonFq4TkI1HLsWXKbRFrh45wzhyXYeI4lJzAcS8ttwKHFXiKxgTmH7XAym0TyZuPcNqAWoHVADx7GqYVH/iFQyz+QlgQzaUuitEgwNjADDbdj4zliJs0I1ZKAVwsP0GF/+w4nsIECuedMumQb0C8GCXj8wsN+/AHjjG+H7eWbgVH5o8Kanx8YYhIfamxwagEC9h9AIhFVQQJu5XBgT4SaUTAKRsEoGKkAAMojU7fsXt0/AAAAAElFTkSuQmCC","orcid":"","institution":"Shenyang University","correspondingAuthor":true,"prefix":"","firstName":"Fynn","middleName":"","lastName":"Fei","suffix":""},{"id":264785969,"identity":"83b1384d-dee1-45ad-9ac0-13e322c6602d","order_by":2,"name":"Huo Yan","email":"","orcid":"","institution":"Shenyang University","correspondingAuthor":false,"prefix":"","firstName":"Huo","middleName":"","lastName":"Yan","suffix":""},{"id":264785970,"identity":"7d8b4f5b-81ed-4c96-9f49-b344b5458f3b","order_by":3,"name":"Wang Xiaotian","email":"","orcid":"","institution":"Shenyang University","correspondingAuthor":false,"prefix":"","firstName":"Wang","middleName":"","lastName":"Xiaotian","suffix":""}],"badges":[],"createdAt":"2023-12-31 07:59:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3825404/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3825404/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50333115,"identity":"607be934-2220-4196-a95f-c4c022827136","added_by":"auto","created_at":"2024-01-29 22:52:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":785368,"visible":true,"origin":"","legend":"","description":"","filename":"Improvementofthewhaleoptimizationalgorithmanditsapplicationtoengineeringdesignproblems.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3825404/v1_covered_94d0c782-4153-4cff-9940-869e3bdcdd0c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Improvement of the whale optimization algorithm and its application to engineering design problems","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":"whale optimization algorithm, adaptive weighting, neighbourhood search, benchmarking functions, engineering design problems","lastPublishedDoi":"10.21203/rs.3.rs-3825404/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3825404/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAiming at the problems of insufficient global exploration ability, low convergence accuracy and slow speed of the standard whale optimization algorithm, the paper proposes a dimension-based neighborhood search strategy, which constructs a neighborhood for each search agent during iteration, and the search agents in this neighborhood can share the search information; considering that the motion of the search agent is a kind of jumping movement assuming successive jumps, which may cause the search agent to prematurely fall into local optimum, so adaptive weights are added to regulate the position update. 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