SLDPSO-TA: Track Assignment Based on Social Learning Discrete Particle Swarm Optimization

SLDPSO-TA: Track Assignment Based on Social Learning Discrete Particle Swarm Optimization | 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 SLDPSO-TA: Track Assignment Based on Social Learning Discrete Particle Swarm Optimization Genggeng Liu, Yidan Jing, Ruping Zhou, Xiaohua Chen, Jeng-Shyang Pan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2197088/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract In the modern Very Large Scale Integration (VLSI) circuit design, the short-circuit problem is one of the key factors affecting routability. Due to the increased circuit size and net density, the short-circuit problem grows significantly in detailed routing. Furthermore, global routing ignores many problems in detailed routing, making for an increased mismatch between global routing and detailed routing. As a crucial intermediate phase between global routing and detailed routing, track assignment is an excellent phase to pre-process the short-circuit problem. However, existing track assignment algorithms face the problem of falling into local optimality. As one of the typical representatives of the swarm intelligence techniques, Particle Swarm Optimization (PSO) is a powerful tool to solve large-scale discrete problems. Therefore, we propose an effective Track Assignment Algorithm Based on Social Learning Discrete Particle Swarm Optimization (SLDPSO-TA). First, the proposed algorithm considers local nets to better guide detailed routing and an effective encoding method is designed to adapt the evolutionary algorithms better. Second, the social learning mode based on example pool mechanism is presented to improve the algorithm performance. Finally, a negotiation-based refining strategy is utilized to further reduce the overlap. Experimental results show that SLDPSO-TA can obtain the best overlap cost optimization among similar works, and reduce the congestion in key routing areas. discrete particle swarm optimization social learning VLSI routing track assignment Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 07 May, 2023 Reviewers invited by journal 07 May, 2023 Editor assigned by journal 27 Oct, 2022 First submitted to journal 23 Oct, 2022 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-2197088","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":188943105,"identity":"1c670555-8495-48ce-b2be-f767a20fc6ba","order_by":0,"name":"Genggeng Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYBACPmYEm/FBBZhOwK+FDUkLs8EZorQgsyWI08LOY/zi445au37p9msVB/4cZuBnzzFg+LkDn8N4zCxnnjmePHPOmbIbB3gOM0j2vDFg7D2DX4sxb9uxZIMbOWm3P0gcZgAyDJgZ2who+QvUYg/UUnDA4DCDPRFajB8zttXYGUikH2M4kAC0RYKgFrYyxt62AwkSN3KYJQ4cSOeROPOs4GAvHi38/Ic3f/jZVmfPPyP94YcDf6zl+NuTNz74iUcLyCIJBobDiQ0MPAYgHg+IOIBXAzDSPzAw1NkzMLA/IKBwFIyCUTAKRioAAOTkUWSg4B/vAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-3099-4371","institution":"Fuzhou University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Genggeng","middleName":"","lastName":"Liu","suffix":""},{"id":188943106,"identity":"3ca42ecf-f886-4e6b-ba4a-cce6a474451b","order_by":1,"name":"Yidan Jing","email":"","orcid":"","institution":"Fuzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yidan","middleName":"","lastName":"Jing","suffix":""},{"id":188943107,"identity":"0b66c732-6dc5-428f-ad4e-97e37e759dc9","order_by":2,"name":"Ruping Zhou","email":"","orcid":"","institution":"Fuzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ruping","middleName":"","lastName":"Zhou","suffix":""},{"id":188943108,"identity":"5f22c6cb-39b9-496f-80aa-bc1e238356fa","order_by":3,"name":"Xiaohua Chen","email":"","orcid":"","institution":"Fuzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaohua","middleName":"","lastName":"Chen","suffix":""},{"id":188943109,"identity":"51620c01-f51a-49aa-9815-0c92f9b71b34","order_by":4,"name":"Jeng-Shyang Pan","email":"","orcid":"","institution":"Shandong University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jeng-Shyang","middleName":"","lastName":"Pan","suffix":""}],"badges":[],"createdAt":"2022-10-24 04:22:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2197088/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2197088/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":35318761,"identity":"df887f4f-3642-48a0-9db3-9e107ad9dc6f","added_by":"auto","created_at":"2023-04-05 09:21:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":714562,"visible":true,"origin":"","legend":"","description":"","filename":"snarticle.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2197088/v1_covered.pdf"}],"financialInterests":"","formattedTitle":"SLDPSO-TA: Track Assignment Based on Social Learning Discrete Particle Swarm Optimization","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":true,"isPdf":true,"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":"discrete particle swarm optimization, social learning, VLSI routing, track assignment","lastPublishedDoi":"10.21203/rs.3.rs-2197088/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2197088/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In the modern Very Large Scale Integration (VLSI) circuit design, the short-circuit problem is one of the key factors affecting routability. Due to the increased circuit size and net density, the short-circuit problem grows significantly in detailed routing. Furthermore, global routing ignores many problems in detailed routing, making for an increased mismatch between global routing and detailed routing. As a crucial intermediate phase between global routing and detailed routing, track assignment is an excellent phase to pre-process the short-circuit problem. However, existing track assignment algorithms face the problem of falling into local optimality. As one of the typical representatives of the swarm intelligence techniques, Particle Swarm Optimization (PSO) is a powerful tool to solve large-scale discrete problems. Therefore, we propose an effective Track Assignment Algorithm Based on Social Learning Discrete Particle Swarm Optimization (SLDPSO-TA). 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