LSHADE-PS-QS: LSHADE with Population-enhancing Strategy and Quality-enhancing Strategy

LSHADE-PS-QS: LSHADE with Population-enhancing Strategy and Quality-enhancing Strategy | 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 LSHADE-PS-QS: LSHADE with Population-enhancing Strategy and Quality-enhancing Strategy Yang Cao, Hedong Peng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9260184/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 8 You are reading this latest preprint version Abstract Differential Evolution (DE) is one of the most effective metaheuristics for continuous optimization. Among its advanced variants, LSHADE has shown strong performance owing to success-history based parameter adaptation and linear population size reduction (LPSR). This paper proposes LSHADE-PS-QS, an enhanced LSHADE variant with a dual-module mechanism for stagnation handling. Specifically, a Quality-enhancing Strategy (QS) is introduced at the individual level to improve mutation quality through Q-learning-based action selection, elite guidance, and plateau perturbation, while a Population-enhancing Strategy (PS) is designed at the population level to maintain diversity through population expansion, partial restart, and population reduction. To further cope with the non-stationarity caused by LPSR, an LPSR-aware Q-value decay mechanism is incorporated. Experimental results on the CEC 2014 and CEC 2017 benchmark suites demonstrate that LSHADE-PS-QS is highly competitive and often superior to representative baseline algorithms over different dimensions and evaluation budgets. Differential Evolution Reinforcement Learning Adaptive Strategy Numerical Optimization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Reviews received at journal 11 Apr, 2026 Reviewers agreed at journal 11 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers invited by journal 09 Apr, 2026 Editor invited by journal 08 Apr, 2026 Editor assigned by journal 01 Apr, 2026 Submission checks completed at journal 01 Apr, 2026 First submitted to journal 29 Mar, 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-9260184","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":622129178,"identity":"1356a230-c5c0-47cc-8bce-4b1a85d5a691","order_by":0,"name":"Yang Cao","email":"","orcid":"","institution":"Shenyang Jianzhu University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Cao","suffix":""},{"id":622129179,"identity":"229b3f65-975b-4e08-ae68-91b50518696e","order_by":1,"name":"Hedong Peng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAk0lEQVRIiWNgGAWjYLACxgZmOdK1GJOuJbGBaNUGN3IMmAt3WKf3HU9g/PAxh1gtM8+k584884BZcuY2IrSY3c7dwMzbdjh3w40ENmZeUrSkG5CsJYF4Lfb33384PLMt3XDmmYfNxPlFsudY4uPCNmt5vuPJBz98JEYLCBwGkwdIiBoGZoiWBOJ1jIJRMApGwcgCAMPeOmibePqZAAAAAElFTkSuQmCC","orcid":"","institution":"Shenyang Jianzhu University","correspondingAuthor":true,"prefix":"","firstName":"Hedong","middleName":"","lastName":"Peng","suffix":""}],"badges":[],"createdAt":"2026-03-29 16:53:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9260184/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9260184/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107481260,"identity":"df5d2607-da9d-4444-bf21-ca513074b347","added_by":"auto","created_at":"2026-04-22 02:16:45","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11534042,"visible":true,"origin":"","legend":"","description":"","filename":"hedongpeng.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9260184/v1_covered_90152f19-a86c-4e40-8256-44833a4a1621.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"LSHADE-PS-QS: LSHADE with Population-enhancing Strategy and Quality-enhancing Strategy","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":"discover-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Computing](https://link.springer.com/journal/10791)","snPcode":"10791","submissionUrl":"https://submission.springernature.com/new-submission/10791/3","title":"Discover Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Differential Evolution, Reinforcement Learning, Adaptive Strategy, Numerical Optimization","lastPublishedDoi":"10.21203/rs.3.rs-9260184/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9260184/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDifferential Evolution (DE) is one of the most effective metaheuristics for continuous optimization. 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