DEHHO: A Modular HHO Variant with Trend-Guided DE Exploitation and Gaussian-Stochastic Exploration

DEHHO: A Modular HHO Variant with Trend-Guided DE Exploitation and Gaussian-Stochastic Exploration | 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 DEHHO: A Modular HHO Variant with Trend-Guided DE Exploitation and Gaussian-Stochastic Exploration Feng Kang, Xin Su This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7038259/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract This paper presents DEHHO, a modular variant of Harris Hawks Optimization (HHO), tailored for high-dimensional and structurally complex optimization tasks. DEHHO integrates two phase-specific strategies: a stochastic Gaussian perturbation mechanism to boost diversity during exploration and a trend-guided DE/current-to-best/1 update for intensified yet adaptive exploitation. A dynamic balancing scheme further coordinates the use of DE and HHO components to maintain search synergy throughout iterations. Many tests on the CEC2017 and CEC2020 benchmarks demonstrate that DEHHO consistently does better than 18 HHO variants and 8 popular metaheuristics in terms of accuracy, reliability, and efficiency. Ablation studies confirm the individual effectiveness and synergistic contribution of each mechanism, underscoring the framework’s interpretability, modularity, and scalability. Physical sciences/Engineering Physical sciences/Mathematics and computing Harris Hawks Optimization Differential Evolution Gaussian Perturbation Trend-Guided Exploitation Modular Metaheuristic Design Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 22 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 03 Dec, 2025 Reviews received at journal 01 Dec, 2025 Reviewers agreed at journal 01 Dec, 2025 Reviews received at journal 04 Sep, 2025 Reviewers agreed at journal 28 Jul, 2025 Reviewers invited by journal 27 Jul, 2025 Editor assigned by journal 18 Jul, 2025 Editor invited by journal 15 Jul, 2025 Submission checks completed at journal 08 Jul, 2025 First submitted to journal 08 Jul, 2025 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. 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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-7038259","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":492081630,"identity":"65cca08b-23a4-4b4d-aade-612d4252a8de","order_by":0,"name":"Feng Kang","email":"","orcid":"","institution":"Chengdu Aeronautical Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Kang","suffix":""},{"id":492081632,"identity":"6f219b68-a663-490b-8abc-0fd273dc6c8c","order_by":1,"name":"Xin Su","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBACfv7GhgMJBjZyEC4bEVokZxw++OBDQZox8VoMDqQlG874cDixgWgtDAfOmEnzGKSl97efPcDwoewwA//sBvw6GJt7QFpscmecyUtgnHHuMIPEnQP4tTAzQGzJ3cCQY8DM23aYwUAiAb8WNoYckJbD6Qb8bwyY/xKjhYcB5H2DwwkGEkBbGInRIiEBCmSDNMMZN94lHOw5l84jcYOAFvvzoKj8YyPP35978MGPMms5/hkEtKC48QCYJAGQpHgUjIJRMApGEgAAm2ZFuAmWIv8AAAAASUVORK5CYII=","orcid":"","institution":"Chengdu Aeronautic Polytechnic University","correspondingAuthor":true,"prefix":"","firstName":"Xin","middleName":"","lastName":"Su","suffix":""}],"badges":[],"createdAt":"2025-07-03 12:38:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7038259/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7038259/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-35565-8","type":"published","date":"2026-01-22T15:58:30+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":101151855,"identity":"e8ea1856-4b69-4007-8b6d-379b402e5d2a","added_by":"auto","created_at":"2026-01-26 16:06:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1367110,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7038259/v1_covered_b06104cf-5b85-4ca7-88e5-f5652a23b45c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"DEHHO: A Modular HHO Variant with Trend-Guided DE Exploitation and Gaussian-Stochastic Exploration","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":"Harris Hawks Optimization, Differential Evolution, Gaussian Perturbation, Trend-Guided Exploitation, Modular Metaheuristic Design","lastPublishedDoi":"10.21203/rs.3.rs-7038259/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7038259/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper presents DEHHO, a modular variant of Harris Hawks Optimization (HHO), tailored for high-dimensional and structurally complex optimization tasks. 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