Hybrid Optimization Algorithm for solving Path Planning Problems Based on Grey Wolf Optimization Algorithm

Hybrid Optimization Algorithm for solving Path Planning Problems Based on Grey Wolf Optimization Algorithm | 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 Hybrid Optimization Algorithm for solving Path Planning Problems Based on Grey Wolf Optimization Algorithm Gang Cheng, Yadong Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4691285/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 The grey wolf optimization algorithm is a heuristic optimization algorithm based on the behavior of grey wolf groups in nature. It has the advantages of a simple concept and few adjustment parameters, and it is widely used in a variety of fields. To address the above shortcomings, this study proposes an improved grey wolf optimization algorithm that uses the gold migration formula from the gold mining optimization algorithm and incorporates chaotic mapping, the gold mining optimization algorithm, the vertical and horizontal crossover strategy, and the Gaussian mutation. Chaos mapping is used to initialize the grey wolf population, ensuring that it is more evenly distributed across the search space. The grey wolf algorithm's α-wolf is updated with the gold migration formula from the gold mining optimization algorithm, increasing its diversity. Horizontal crossover is used for searching, which reduces the algorithm's blind zone and improves its global search capability. Vertical crossover prevents the algorithm from converging prematurely. The introduction of the Gaussian mutation effectively prevents the algorithm from falling into the local optimum premature problem. To determine the algorithm's effectiveness, this study compares the improved Grey Wolf optimization algorithm to other Grey Wolf optimization algorithms on 23 benchmark functions. After experimental verification, the proposed algorithm outperforms the other comparative algorithms. Meanwhile, when the algorithm is applied to path planning, the paths generated are shorter, and the running time is shorter than that of other algorithms, demonstrating the algorithm's applicability. grey wolf optimization algorithm gold rush optimization algorithm chaotic mapping vertical and horizontal strategies Gaussian mutation Full Text Additional Declarations No competing interests reported. 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. 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-4691285","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":332637559,"identity":"49784df3-85b0-4782-b1d2-2007ddeccf80","order_by":0,"name":"Gang Cheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYBAC9gYILQPEjA8SKmoIa+E5AKWBmNngwZljpGlhk3zYwkyEFvbew6952+7w8Eu3X6tIbGBj4G/vTsCvhedcmjVv2zMeyTlnym4k7pBhkDhzdgNeLfYSOWbGvG2HeQxu5KTdSDzDxmAgkYtfC4/8G4gWe6CWgsQ2ZiK0SPAYPwbbIpF+jIE4LTw5Zoxzzh3mkbiRwyyRcOYYD0G/8LCfMf7wpuywHP+M9Icff1TUyPG39+LXAgRsUjwQ3QZgkpByEGD++ANMsz8gRvUoGAWjYBSMQAAAwv9HN4KroyMAAAAASUVORK5CYII=","orcid":"","institution":"Tibet University","correspondingAuthor":true,"prefix":"","firstName":"Gang","middleName":"","lastName":"Cheng","suffix":""},{"id":332637560,"identity":"a50b3e3c-04de-40df-be34-317d5f503d61","order_by":1,"name":"Yadong Liu","email":"","orcid":"","institution":"Tibet University","correspondingAuthor":false,"prefix":"","firstName":"Yadong","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-07-05 09:38:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4691285/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4691285/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73427712,"identity":"3339a2de-b124-4b12-998e-53323f609808","added_by":"auto","created_at":"2025-01-09 21:46:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1598317,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4691285/v1_covered_6d5ed886-6320-43de-9306-7835502cb8f6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hybrid Optimization Algorithm for solving Path Planning Problems Based on Grey Wolf Optimization Algorithm","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":"grey wolf optimization algorithm, gold rush optimization algorithm, chaotic mapping, vertical and horizontal strategies, Gaussian mutation","lastPublishedDoi":"10.21203/rs.3.rs-4691285/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4691285/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The grey wolf optimization algorithm is a heuristic optimization algorithm based on the behavior of grey wolf groups in nature. 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