Multifactorial Memetic Algorithm with Adaptive Auxiliary Tasks for Service Migration Optimization in Mobile Edge Computing

Multifactorial Memetic Algorithm with Adaptive Auxiliary Tasks for Service Migration Optimization in Mobile Edge Computing | 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 Method Article Multifactorial Memetic Algorithm with Adaptive Auxiliary Tasks for Service Migration Optimization in Mobile Edge Computing Guo Li, Zhaobo Liu, Ling Liu, Zexuan Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4212598/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Mar, 2025 Read the published version in Memetic Computing → Version 1 posted 12 You are reading this latest preprint version Abstract In high-speed mobile networks, mobile edge computing is tasked with service migration optimization, i.e., assigning mobile users to the right servers to minimize the response time. Service migration optimization is a complex problem posing significant challenges to conventional optimization methods. To tackle this problem , we develop a multifactorial memetic algorithm with adaptive auxiliary tasks or MFMA-AAT for short. MFMA-AAT solves the target service migration optimization problem and an adaptively selected auxiliary task simultaneously, where the auxiliary task is a simplified version of the target problem to guide the search towards promising regions faster via knowledge transfer. Multiple auxiliary tasks are pre-constructed based on the distribution of the mobile users and the one with best improvement at each generation is selected for knowledge transfer. A community detection based memetic operator is also introduced to accelerate the local convergence of the proposed algorithm. Experimental results on test problems demonstrate that MFMA-AAT is more efficient than traditional service migration approaches and other state-of-the-art multifactorial evolutionary algorithms. Evolutionary multitasking Multifactorial evolutionary algorithm Multi-form evolutionary optimization Service migration optimization Mobile edge computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Mar, 2025 Read the published version in Memetic Computing → Version 1 posted Editorial decision: Revision requested 30 Sep, 2024 Reviews received at journal 10 Sep, 2024 Reviews received at journal 06 Sep, 2024 Reviewers agreed at journal 26 Aug, 2024 Reviews received at journal 26 Aug, 2024 Reviewers agreed at journal 22 Aug, 2024 Reviewers agreed at journal 07 May, 2024 Reviewers agreed at journal 07 May, 2024 Reviewers invited by journal 07 May, 2024 Editor assigned by journal 01 May, 2024 Submission checks completed at journal 04 Apr, 2024 First submitted to journal 03 Apr, 2024 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-4212598","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":287453445,"identity":"21c4fb6a-11d2-42d8-80df-75f82bafd1ea","order_by":0,"name":"Guo Li","email":"","orcid":"","institution":"Shenzhen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Guo","middleName":"","lastName":"Li","suffix":""},{"id":287453446,"identity":"ced3ae61-fc4d-4b36-a890-eaf14f3c13aa","order_by":1,"name":"Zhaobo Liu","email":"","orcid":"","institution":"Shenzhen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhaobo","middleName":"","lastName":"Liu","suffix":""},{"id":287453447,"identity":"3ccf9f34-e022-441d-8a4e-d173f61dda66","order_by":2,"name":"Ling Liu","email":"","orcid":"","institution":"Shenzhen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Liu","suffix":""},{"id":287453448,"identity":"b3bc9fed-3694-4b8e-b4d6-fd72d995ed7f","order_by":3,"name":"Zexuan Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYDACCRBRgcQmUssZkrUwtpGiRX52j/Fn3nl19gYHmA/e5mGwyyOohXHOGQPDmdsOMxscYEu25mFILiaohVkixyDh47YDbAYHeMykeRgOJDYQ0sIG1HIgcU4dj8EB/m/EaeGRyDFs+NjALAG0hY04LRISacWMM44dNpA8zGZsOccgmbAW+RnJmz/z1NTZ8x1vfnjjTYUdYS0IwAwiDIhXPwpGwSgYBaMADwAAd3AzJLtPoxUAAAAASUVORK5CYII=","orcid":"","institution":"Shenzhen University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Zexuan","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2024-04-03 12:06:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4212598/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4212598/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12293-025-00448-4","type":"published","date":"2025-03-21T15:58:24+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79120613,"identity":"61b08df9-d967-472b-b5c4-5eb44cff6f88","added_by":"auto","created_at":"2025-03-24 16:10:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3419089,"visible":true,"origin":"","legend":"","description":"","filename":"MFMAAATMemetComput1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4212598/v1_covered_a448e9ce-0c3a-461a-88c8-d2e42c0c5444.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multifactorial Memetic Algorithm with Adaptive Auxiliary Tasks for Service Migration Optimization in Mobile Edge Computing","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":"memetic-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meme","sideBox":"Learn more about [Memetic Computing](http://link.springer.com/journal/12289)","snPcode":"12293","submissionUrl":"https://submission.nature.com/new-submission/12293/3","title":"Memetic Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Evolutionary multitasking, Multifactorial evolutionary algorithm, Multi-form evolutionary optimization, Service migration optimization, Mobile edge computing","lastPublishedDoi":"10.21203/rs.3.rs-4212598/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4212598/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In high-speed mobile networks, mobile edge computing is tasked with service migration optimization, i.e., assigning mobile users to the right servers to minimize the response time. 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