SEHHO-COBL: An Enhanced Harris Hawks Optimization with History-Guided Adaptive Parameter Memory and Chaotic Opposition-Based Learning for Numerical and Engineering Optimization

SEHHO-COBL: An Enhanced Harris Hawks Optimization with History-Guided Adaptive Parameter Memory and Chaotic Opposition-Based Learning for Numerical and Engineering Optimization | 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 SEHHO-COBL: An Enhanced Harris Hawks Optimization with History-Guided Adaptive Parameter Memory and Chaotic Opposition-Based Learning for Numerical and Engineering Optimization Yanxiao Li, Junhao Wei, Yifu Zhao, Zikun Li, Baili Lu, Sio-Kei Im, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9253908/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Harris Hawks Optimization (HHO) offers a structurally elegant escape energy-driven exploration-exploitation transition, yet its underlying siege strategies exhibit weak directionality, rigid step-size control, and progressive diversity loss as dimensionality grows. This paper introduces SEHHO-COBL, which retains HHO's phase division skeleton but entirely replaces the siege mechanism with a history-guided adaptive differential evolution engine: a circular memory bank records successful scaling factors and crossover rates, enabling landscape-responsive parameter self-adaptation via DE/current-to-pbest/1 mutation. The framework is further strengthened by Tent-map chaotic opposition-based learning initialisation, an external archive that enriches differential-vector diversity, and L'{e}vy flight perturbation during the exploration phase. Comparative experiments on the CEC2022 benchmark (12 functions, 10D and 20D, 30 independent runs, 8 competitors) demonstrate that SEHHO-COBL ranks first on 11 of 12 functions at 10D (Friedman 1.25) and on all 12 at 20D (Friedman 1.00; Wilcoxon 96/0/0). A systematic ablation study confirms that the adaptive parameter memory is the most critical module ($p = 0.002$). Application to five constrained engineering design problems ($D = 4$-$38$) yields a perfect first-rank record, validating the algorithm's practical effectiveness. Physical sciences/Engineering Physical sciences/Mathematics and computing Physical sciences/Physics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 10 Apr, 2026 Reviewers invited by journal 08 Apr, 2026 Editor assigned by journal 06 Apr, 2026 Editor invited by journal 06 Apr, 2026 Submission checks completed at journal 02 Apr, 2026 First submitted to journal 02 Apr, 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. 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-9253908","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":621074893,"identity":"8e988114-c96f-4e71-93c3-437bbc8261d4","order_by":0,"name":"Yanxiao Li","email":"","orcid":"","institution":"Macao Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Yanxiao","middleName":"","lastName":"Li","suffix":""},{"id":621074894,"identity":"22b1c3b5-9641-415d-a210-3f9d60b33cb0","order_by":1,"name":"Junhao Wei","email":"","orcid":"","institution":"Macao Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Junhao","middleName":"","lastName":"Wei","suffix":""},{"id":621074895,"identity":"8047ffc6-a3a1-40bb-8cc6-daa8241d2697","order_by":2,"name":"Yifu Zhao","email":"","orcid":"","institution":"Macao Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Yifu","middleName":"","lastName":"Zhao","suffix":""},{"id":621074896,"identity":"536aa69d-8f1b-481f-801e-cb9091580e06","order_by":3,"name":"Zikun Li","email":"","orcid":"","institution":"South China Normal University","correspondingAuthor":false,"prefix":"","firstName":"Zikun","middleName":"","lastName":"Li","suffix":""},{"id":621074897,"identity":"dc6621f6-0f82-46e9-9d44-acbdc30ef935","order_by":4,"name":"Baili Lu","email":"","orcid":"","institution":"Zhongkai University of Agriculture and Engineering","correspondingAuthor":false,"prefix":"","firstName":"Baili","middleName":"","lastName":"Lu","suffix":""},{"id":621074898,"identity":"8a88ccbf-2b01-4ff4-8859-867ec68f6a6a","order_by":5,"name":"Sio-Kei Im","email":"","orcid":"","institution":"Macao Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Sio-Kei","middleName":"","lastName":"Im","suffix":""},{"id":621074899,"identity":"3abd3719-5f57-4756-987c-591e4ce7520e","order_by":6,"name":"Xu Yang","email":"","orcid":"","institution":"Macao Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Xu","middleName":"","lastName":"Yang","suffix":""},{"id":621074900,"identity":"d6e0293c-5153-4fe6-ae64-e851e0b17f1b","order_by":7,"name":"Ka-Hou Chan","email":"","orcid":"","institution":"Macao Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Ka-Hou","middleName":"","lastName":"Chan","suffix":""},{"id":621074901,"identity":"8520cb9e-296e-4356-a175-f3f87c278e4f","order_by":8,"name":"Yapeng Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYHAD5gMQ+gDxWtgSSNbCY0CcFoPjZw8+Lvh1WN6cf83XzYVtDHJ8NxJYN3zAp+VMXrLxzL7DhjtnvN12e2Ybg7HkjQS2mzPwaTmQYybN23ObccONs9tu87YxJG4AarnNg0/L+Tfmv4Fa7DfcOPMMpKWesJYbOWbMPD9uJ24438MG0pJgQEiL5I13ydK8Df+TN9xgM7s945yE4cwzD9vw+oXvfO7Bzzx/0mw3nD/87HZBmY083/HkYzfwhZjCAaAbGNuALIkEBmYgCWQxNuDRwMAg3wBy9h8g5j8A0jIKRsEoGAWjABMAAGfxXUA/q+CSAAAAAElFTkSuQmCC","orcid":"","institution":"Macao Polytechnic University","correspondingAuthor":true,"prefix":"","firstName":"Yapeng","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-03-28 15:38:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9253908/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9253908/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107031359,"identity":"01b86f7e-923c-4a5d-a4a7-f3581bb10dee","added_by":"auto","created_at":"2026-04-16 03:01:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13146021,"visible":true,"origin":"","legend":"","description":"","filename":"TemplateforsubmissionstoScientificReports.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9253908/v1_covered_47a79f7f-2a3e-4556-915c-f370a3ebbe33.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"SEHHO-COBL: An Enhanced Harris Hawks Optimization with History-Guided Adaptive Parameter Memory and Chaotic Opposition-Based Learning for Numerical and Engineering Optimization","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":"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":"","lastPublishedDoi":"10.21203/rs.3.rs-9253908/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9253908/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Harris Hawks Optimization (HHO) offers a structurally elegant escape energy-driven exploration-exploitation transition, yet its underlying siege strategies exhibit weak directionality, rigid step-size control, and progressive diversity loss as dimensionality grows. This paper introduces SEHHO-COBL, which retains HHO's phase division skeleton but entirely replaces the siege mechanism with a history-guided adaptive differential evolution engine: a circular memory bank records successful scaling factors and crossover rates, enabling landscape-responsive parameter self-adaptation via DE/current-to-pbest/1 mutation. The framework is further strengthened by Tent-map chaotic opposition-based learning initialisation, an external archive that enriches differential-vector diversity, and L\\'{e}vy flight perturbation during the exploration phase. Comparative experiments on the CEC2022 benchmark (12 functions, 10D and 20D, 30 independent runs, 8 competitors) demonstrate that SEHHO-COBL ranks first on 11 of 12 functions at 10D (Friedman 1.25) and on all 12 at 20D (Friedman 1.00; Wilcoxon 96/0/0). A systematic ablation study confirms that the adaptive parameter memory is the most critical module ($p = 0.002$). Application to five constrained engineering design problems ($D = 4$-$38$) yields a perfect first-rank record, validating the algorithm's practical effectiveness.","manuscriptTitle":"SEHHO-COBL: An Enhanced Harris Hawks Optimization with History-Guided Adaptive Parameter Memory and Chaotic Opposition-Based Learning for Numerical and Engineering Optimization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-16 03:01:45","doi":"10.21203/rs.3.rs-9253908/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"67912516520164448473029097998652840953","date":"2026-04-10T15:15:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-08T13:19:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-06T10:18:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-06T10:05:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-02T08:40:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-04-02T08:21:44+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"06eb6848-2c1e-4e66-bd0e-2b729ff82b90","owner":[],"postedDate":"April 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":66101077,"name":"Physical sciences/Engineering"},{"id":66101078,"name":"Physical sciences/Mathematics and computing"},{"id":66101079,"name":"Physical sciences/Physics"}],"tags":[],"updatedAt":"2026-05-12T04:38:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-16 03:01:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9253908","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9253908","identity":"rs-9253908","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}