Heterogeneous ant colony optimization based on adaptive interactive learning and non-zero-sum game

Heterogeneous ant colony optimization based on adaptive interactive learning and non-zero-sum game | 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 Heterogeneous ant colony optimization based on adaptive interactive learning and non-zero-sum game Jingwen Meng, Xiaoming You, Sheng Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-533743/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Feb, 2022 Read the published version in Soft Computing → Version 1 posted 4 You are reading this latest preprint version Abstract Ant colony optimization (ACO) is prone to get into the local optimum and has a slow convergence speed when it is applied to the Travelling Salesman Problem (TSP). Therefore, for overcoming the drawbacks of ACO, a heterogeneous ant colony optimization based on adaptive interactive learning and non-zero-sum game is proposed. Firstly, three subpopulations with different characteristics are constructed into heterogeneous ant colony to enhance the performance of the ant colony. Secondly, the adaptive interactive learning mechanism is adopted when the algorithm diversity decreases, in which the objects to be communicated are selected adaptively according to the population similarity. In this mechanism, the way of communication is to pair the inferior individuals with the superior individuals, which enlarges the searching range and speeds up the convergence speed. Finally, an elite information exchange strategy based on non-zero-sum game is adopted when the algorithm falls into local optimum, in which each subpopulation selects the partners for elite information exchange according to the normalized comprehensive evaluation operator, which is helpful for each subpopulation to select the most appropriate strategy for getting out of the local optimal. Through this model, the accuracy of the solution is further improved. The data that used for this experiment is from the TSPLIB library under MATLAB simulation with various ranges of TSP datasets. Experimental results indicate that the proposed algorithm has a higher quality solution and faster convergence speed in solving the traveling salesman problem. Electrical Engineering Ant colony algorithm Adaptive interactive learning Elite information exchange Non-zero-sum game TSP Full Text Cite Share Download PDF Status: Published Journal Publication published 27 Feb, 2022 Read the published version in Soft Computing → Version 1 posted Reviewers invited by journal 23 Jun, 2021 Reviews received at journal 23 Jun, 2021 Editor assigned by journal 17 May, 2021 First submitted to journal 16 May, 2021 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-533743","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":35493378,"identity":"c37caec6-03e7-4654-9ab3-74ace759412d","order_by":0,"name":"Jingwen Meng","email":"","orcid":"","institution":"Shanghai University of Engineering Science","correspondingAuthor":false,"prefix":"","firstName":"Jingwen","middleName":"","lastName":"Meng","suffix":""},{"id":35493379,"identity":"8378bc04-4f47-4537-bf58-44e59f112670","order_by":1,"name":"Xiaoming You","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYHACxocfDCTk2NjbDxCthdlYosLCmI/nTALRWtgEeM5UJM6TcDAgTr3Bjew0Bsk2ifQ2CYYEhh8V24jQcubstgeFbRK5bdKNBxh7ztwmQsvx3u0GkiAtMgcSmBnbiNFymHebBC/QYWwSCQZEajneu02C54xEAvFaJM+c3QwMZAnDNmAgHyTKL3w3cjcCo7JOXr69/eCDHxVEaFE4gMQ5gEMRKpBvIErZKBgFo2AUjGgAAMO9PXjI5oq6AAAAAElFTkSuQmCC","orcid":"","institution":"Shanghai University of Engineering Science","correspondingAuthor":true,"prefix":"","firstName":"Xiaoming","middleName":"","lastName":"You","suffix":""},{"id":35493380,"identity":"f5aab1e3-6c16-40bf-a942-5e12e5df726b","order_by":2,"name":"Sheng Liu","email":"","orcid":"","institution":"Shanghai University of Engineering Science","correspondingAuthor":false,"prefix":"","firstName":"Sheng","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2021-05-17 05:48:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-533743/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-533743/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00500-022-06833-2","type":"published","date":"2022-02-28T03:51:04+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":18668746,"identity":"4b5a901b-bde0-4359-84bf-644ac1acf28f","added_by":"auto","created_at":"2022-02-28 03:51:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":111480,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-533743/v1/b587ed98-0e9b-4129-a974-521f0d2b7e2f.pdf"}],"financialInterests":"","formattedTitle":"Heterogeneous ant colony optimization based on adaptive interactive learning and non-zero-sum game","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-533743/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"soft-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"soco","sideBox":"Learn more about [Soft Computing](https://www.springer.com/journal/500)","snPcode":"500","submissionUrl":"https://submission.nature.com/new-submission/500/3","title":"Soft Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Ant colony algorithm, Adaptive interactive learning, Elite information exchange, Non-zero-sum game, TSP ","lastPublishedDoi":"10.21203/rs.3.rs-533743/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-533743/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Ant colony optimization (ACO) is prone to get into the local optimum and has a slow convergence speed when it is applied to the Travelling Salesman Problem (TSP). Therefore, for overcoming the drawbacks of ACO, a heterogeneous ant colony optimization based on adaptive interactive learning and non-zero-sum game is proposed. Firstly, three subpopulations with different characteristics are constructed into heterogeneous ant colony to enhance the performance of the ant colony. Secondly, the adaptive interactive learning mechanism is adopted when the algorithm diversity decreases, in which the objects to be communicated are selected adaptively according to the population similarity. In this mechanism, the way of communication is to pair the inferior individuals with the superior individuals, which enlarges the searching range and speeds up the convergence speed. Finally, an elite information exchange strategy based on non-zero-sum game is adopted when the algorithm falls into local optimum, in which each subpopulation selects the partners for elite information exchange according to the normalized comprehensive evaluation operator, which is helpful for each subpopulation to select the most appropriate strategy for getting out of the local optimal. Through this model, the accuracy of the solution is further improved. The data that used for this experiment is from the TSPLIB library under MATLAB simulation with various ranges of TSP datasets. 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