DMPA-HHO: A Hybrid MPA-HHO optimizer with Dynamic Opposite Learning

DMPA-HHO: A Hybrid MPA-HHO optimizer with Dynamic Opposite Learning | 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 DMPA-HHO: A Hybrid MPA-HHO optimizer with Dynamic Opposite Learning Shoubao Su, Liukai Xu, Chishe Wang, Chao He This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2280911/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 A new hybrid algorithm is proposed by incorporating Harris Hawks Optimization with Marine Predators algorithm and dynamic Opposition-based learning, namely DMPA-HHO. In the algorithm, the problem is addressed that Harris Hawks Optimization (HHO) tends to fall into local optima and low accuracy of the solution. Dynamic Opposite Learning (DOL) improves the swarm diversity and swarm quality, and enhances the global search capability and search accuracy. HHO and the Marine Predators Algorithm (MPA) are blended to enhance the progressive rapid dives of the Harris hawk flock, effectively improving the algorithm's exploitation capabilities. DMPA-HHO uses the FADs’ effect of the MPA to increase the possibility of individuals escaping from the local optimum solution when the search falls into the local optimal solution. Compared with others on several benchmark functions, the DMPA-HHO algorithm has a better search accuracy and a stronger ability to avoid trapping in local optima. Physical sciences/Mathematics and computing/Information technology Physical sciences/Mathematics and computing/Computer science Harris Hawks Optimization (HHO) Marine Predators Algorithm (MPA) Dynamic Opposite Learning (DOL) Fish Aggregating Devices (FADs) swarm intelligent optimization Full Text Additional Declarations No competing interests reported. Supplementary Files mphhosrsupp.pdf 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. 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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-2280911","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":159615057,"identity":"dc1e4d4d-b909-498f-88a3-3aff62356352","order_by":0,"name":"Shoubao Su","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYDACCQYGZgYGG2Y2EIeHBC1pzGxsJGo5zMBAtBb52c3HpAsqzrPzyTcwPnjbxiBvTkiLwZ1jadIzztwGOYzZcG4bg+HOBkJaJHLMbvO2gbWwSfO2MSQYHCDksBlgLedAWth/E6WF4QZYywGwLcxEaTG4kZb+e8aZZKCWxGbJOeckDDcQdljyYeOCCrtk+ebDBz+8KbORJ+wwKEhmYGBsYABHE7HAjnilo2AUjIJRMOIAAD21NVxqKcuSAAAAAElFTkSuQmCC","orcid":"","institution":"Jinling Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Shoubao","middleName":"","lastName":"Su","suffix":""},{"id":159615059,"identity":"b2533c61-5512-458a-8ae1-a9985a30a2c0","order_by":1,"name":"Liukai Xu","email":"","orcid":"","institution":"Jinling Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Liukai","middleName":"","lastName":"Xu","suffix":""},{"id":159615060,"identity":"00a4d6e6-6f81-4eb4-948d-4bf63efbb794","order_by":2,"name":"Chishe Wang","email":"","orcid":"","institution":"Jinling Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Chishe","middleName":"","lastName":"Wang","suffix":""},{"id":159615062,"identity":"6fe9b9fe-d9c2-42c7-8bd1-e2ba08a14ba8","order_by":3,"name":"Chao He","email":"","orcid":"","institution":"Jiangsu University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2022-11-16 14:44:18","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false,"coiExplicitlySet":false},"doi":"10.21203/rs.3.rs-2280911/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2280911/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":44913938,"identity":"2bb6957d-5daf-4672-ab02-1647d92673aa","added_by":"auto","created_at":"2023-10-19 10:22:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1293933,"visible":true,"origin":"","legend":"","description":"","filename":"MPHHOsr.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2280911/v1_covered_a3059087-4090-40aa-bc4b-72db0b5d0fec.pdf"},{"id":30653337,"identity":"a4a330d5-57f1-462a-b9e9-a437ac13f324","added_by":"auto","created_at":"2022-12-22 03:23:22","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":213031,"visible":true,"origin":"","legend":"","description":"","filename":"mphhosrsupp.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2280911/v1/d02361ece327627a82c8caea.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"DMPA-HHO: A Hybrid MPA-HHO optimizer with Dynamic Opposite Learning","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":"Harris Hawks Optimization (HHO), Marine Predators Algorithm (MPA), Dynamic Opposite Learning (DOL), Fish Aggregating Devices (FADs), swarm intelligent optimization","lastPublishedDoi":"10.21203/rs.3.rs-2280911/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2280911/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"A new hybrid algorithm is proposed by incorporating Harris Hawks Optimization with Marine Predators algorithm and dynamic Opposition-based learning, namely DMPA-HHO. 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