Optimization of Robotic Arm Scheduling Based on Catfish Optimization Algorithm and Q-Learning

Optimization of Robotic Arm Scheduling Based on Catfish Optimization Algorithm and Q-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 Research Article Optimization of Robotic Arm Scheduling Based on Catfish Optimization Algorithm and Q-Learning Lu Kong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9242643/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract In order to improve the intelligence and efficiency of robot scheduling in workshop production lines, the author proposes a robot scheduling optimization based on the fish optimization algorithm and Q-learning. Firstly, conduct a thorough analysis of the characteristics of workshop scheduling problems and construct a mathematical model that includes multiple objectives such as minimum completion time and utilization rate of robotic arms. Based on this model, the author designed and implemented a scheduling optimization method that combines catfish optimization algorithm with Q-learning. The innovation of the research lies in integrating these two algorithms to form an adaptive scheduling strategy. By dynamically adjusting the mutation rate in Q-learning and combining it with the population search capability of the fish algorithm, the adaptive ability and global search efficiency of the scheduling process are improved. The experimental results show that in small-scale problems, the hybrid algorithm reduces the average completion time by 8.5% compared to traditional genetic algorithms; In large-scale complex problems, this advantage is even more significant, achieving a 12.7% improvement. Especially when dealing with dynamic scheduling environments, hybrid algorithms exhibit stronger adaptability and robustness. Conclusion: The results of this study are of great significance for promoting the development of intelligent manufacturing technology, improving production efficiency, and reducing production costs. Not only does it enrich the application research of intelligent optimization algorithms in practical engineering problems in theory, but it also provides practical guidance for the transformation of manufacturing industry towards intelligence and flexibility. Robotic arm scheduling Optimization algorithm for catfish Q learning Intelligent manufacturing multi-objective optimization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 15 May, 2026 Reviewers invited by journal 19 Apr, 2026 Editor assigned by journal 15 Apr, 2026 Editor invited by journal 15 Apr, 2026 Submission checks completed at journal 08 Apr, 2026 First submitted to journal 08 Apr, 2026 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-9242643","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":627071886,"identity":"1d1c932f-0ff6-495c-9c34-fb7eaa5b8351","order_by":0,"name":"Lu Kong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYBACAwYeAyBlI8cP4j0ACRChJQFIpRlLNjAwNiQQqeUAkDqcuOEA0VokchsfF/xKS9x8vMf8QUKFjTED++GjG/BryWc2ntlnY7ztzBnDhoQzaWYMPGlpNwhoYZPm7UmT3XYjx7Ahse2wDYMEjxkBLbltQC2HGTfPIF5LzjFpnh+HFTdIQLSYEdbC8ybZmLchzVjizLHCGUC/GLMR8ot9e47hY54/wKhsb97w4UOFjWE/++FjeLWAAWMbEoeNoHIw+EOcslEwCkbBKBihAABJGktzvmCmEgAAAABJRU5ErkJggg==","orcid":"","institution":"Henan College of Industry and Information Technology","correspondingAuthor":true,"prefix":"","firstName":"Lu","middleName":"","lastName":"Kong","suffix":""}],"badges":[],"createdAt":"2026-03-27 09:09:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9242643/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9242643/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107870514,"identity":"b98c5b5f-024c-4deb-be54-bf2b366ac00f","added_by":"auto","created_at":"2026-04-27 07:39:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":590227,"visible":true,"origin":"","legend":"","description":"","filename":"article.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9242643/v1_covered_eb9a6efb-a317-4365-8f40-c150221d71d5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimization of Robotic Arm Scheduling Based on Catfish Optimization Algorithm and Q-Learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"discover-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Computing](https://link.springer.com/journal/10791)","snPcode":"10791","submissionUrl":"https://submission.springernature.com/new-submission/10791/3","title":"Discover Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Robotic arm scheduling, Optimization algorithm for catfish, Q learning, Intelligent manufacturing, multi-objective optimization","lastPublishedDoi":"10.21203/rs.3.rs-9242643/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9242643/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn order to improve the intelligence and efficiency of robot scheduling in workshop production lines, the author proposes a robot scheduling optimization based on the fish optimization algorithm and Q-learning. 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