A Cluster-Driven Dual-Population Differential Evolution Algorithm and Its Applications

A Cluster-Driven Dual-Population Differential Evolution 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 Research Article A Cluster-Driven Dual-Population Differential Evolution Algorithm and Its Applications Chuang Du, Weiliang He, Wenying Zeng, Yujian Du, Mengqun Liu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8521444/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract To overcome the limitations of traditional differential evolution (DE) in highdimensional, multimodal, and constrained continuous optimization problems, This paper proposes ClusterDual-DE, a cluster-driven dual-population DE framework. ClusterDual-DE maintains two complementary subpopulations: (i) a cluster-guided exploitation subpopulation that performs adaptive niching/clustering to intensify local search within promising basins, and (ii) an explorationoriented subpopulation that promotes global search via large-step mutation and a dimension-adaptive restart strategy. The two subpopulations cooperate through three mechanisms: success-history-based parameter adaptation, elite migration with shared memory, and function-evaluation-budget-driven population reduction, which jointly regulate information exchange and allocate computational resources between exploration and exploitation. Extensive experiments on the CEC 2014 and CEC 2017 benchmark suites (10-100 dimensions) show that ClusterDual-DE achieves the lowest average rank in the Friedman test and under the Wilcoxon signed-rank test (e.g., p< 0.05), with multiple-comparison correction if applicable. ClusterDual-DE outperforms nine representative stateof-the-art DE variants in solution quality, robustness, and convergence efficiency. The generality of ClusterDual-DE is further demonstrated in two classical engineering applications (pressure vessel design and automotive side-impact optimization). In addition, This paper evaluates a cooperative unmanned underwater vehicle (UUV) path-planning task. The task involves coupled constraints from 3-D seafloor obstacles, wave effects, and sonar conditions. ClusterDual-DE achieves a lower objective cost and a shorter planning time than nine competing algorithms. The results indicate good adaptability in complex coupled environments. Differential Evolution Dual-Population Optimization Clustering-Based Local Search Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 15 May, 2026 Reviews received at journal 08 May, 2026 Reviews received at journal 06 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviewers agreed at journal 03 May, 2026 Reviewers agreed at journal 03 May, 2026 Reviewers invited by journal 12 Feb, 2026 Editor assigned by journal 12 Feb, 2026 Submission checks completed at journal 07 Jan, 2026 First submitted to journal 05 Jan, 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-8521444","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":591495886,"identity":"afbaf713-3ea8-44ad-8c1b-de095609a139","order_by":0,"name":"Chuang Du","email":"","orcid":"","institution":"Beihang University","correspondingAuthor":false,"prefix":"","firstName":"Chuang","middleName":"","lastName":"Du","suffix":""},{"id":591495893,"identity":"e5447473-bf37-464d-a522-4e181305f88e","order_by":1,"name":"Weiliang He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYBACAxCRUCABJJmPQcUSiNFiANLClkaCFgjJY0acFnP2HsMHDwws5M3513x7zFNjx8DPnmPA8HMHbi2WPWeMDYAOM9w54+12Y55jyQySPW8MGHvP4HHYjdxtEkAtjBtunN0mzcPGDBTJMWBmbMOj5f7b7T+AWuw33DjzTJrnXz2DPUEtN3i3gUIsccP5HjZp3rbDDAYShLScyf8Mcljyhhts5oZz+47zSJx5VnCwF5+W48cSP/6oqLPdcP7wswdvvlXL8bcnb3zwE48WBJBIAFM8IOIAMRoYGPiJVDcKRsEoGAUjDwAAQYBRhfSvxXgAAAAASUVORK5CYII=","orcid":"","institution":"Beihang University","correspondingAuthor":true,"prefix":"","firstName":"Weiliang","middleName":"","lastName":"He","suffix":""},{"id":591495894,"identity":"eedfebcb-7e7e-4647-ad74-755129ffb5a9","order_by":2,"name":"Wenying Zeng","email":"","orcid":"","institution":"Beihang University","correspondingAuthor":false,"prefix":"","firstName":"Wenying","middleName":"","lastName":"Zeng","suffix":""},{"id":591495896,"identity":"b4aeb8bc-1f2d-4b34-bd6c-314d9228cece","order_by":3,"name":"Yujian Du","email":"","orcid":"","institution":"Beihang University","correspondingAuthor":false,"prefix":"","firstName":"Yujian","middleName":"","lastName":"Du","suffix":""},{"id":591495899,"identity":"c355dbf0-9e80-4de1-8e38-13c8234e15b8","order_by":4,"name":"Mengqun Liu","email":"","orcid":"","institution":"Beihang University","correspondingAuthor":false,"prefix":"","firstName":"Mengqun","middleName":"","lastName":"Liu","suffix":""},{"id":591495902,"identity":"3c02736a-ab7c-4113-9cba-4cc861aea866","order_by":5,"name":"Yi Feng","email":"","orcid":"","institution":"Beihang University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Feng","suffix":""}],"badges":[],"createdAt":"2026-01-05 12:38:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8521444/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8521444/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102963281,"identity":"f9674ce4-e169-4b11-8529-b653d024fbe0","added_by":"auto","created_at":"2026-02-19 04:15:10","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":17075797,"visible":true,"origin":"","legend":"","description":"","filename":"snarticletemplate.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8521444/v1_covered_ad5632be-c6bc-4091-b2cc-5d25986b9065.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Cluster-Driven Dual-Population Differential Evolution Algorithm and Its Applications","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":"cluster-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Cluster Computing](https://www.springer.com/journal/10586)","snPcode":"10586","submissionUrl":"https://submission.nature.com/new-submission/10586/3","title":"Cluster Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Differential Evolution,Dual-Population Optimization,Clustering-Based Local Search","lastPublishedDoi":"10.21203/rs.3.rs-8521444/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8521444/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"To overcome the limitations of traditional differential evolution (DE) in highdimensional, multimodal, and constrained continuous optimization problems, This paper proposes ClusterDual-DE, a cluster-driven dual-population DE framework. 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