Heterogeneous Parallel Implementation of PSO Algorithm with an Aging Leader and Challengers

Heterogeneous Parallel Implementation of PSO Algorithm with an Aging Leader and Challengers | 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 Parallel Implementation of PSO Algorithm with an Aging Leader and Challengers Fahimeh Yazdanpanah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2292185/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 Parallel computing techniques provide high-performance execution of large-size and real-world complicated problems such as heuristic optimization algorithms. One of the most popular optimization algorithms is PSO (particle swarm optimization) which is an intelligent search method for finding the best solution according to population state. The PSO algorithm has obtained significant attention from researchers and different parallel implementations of this algorithm have been presented for intensive-computing applications. The ALC-PSO algorithm (PSO with an aging leader and challengers) is an improved population-based and intensive-computation procedure thanks to the high fitness analyses. Compared to the traditional PSO, the ALC-PSO algorithm improves convergence rapidity. In this paper, we have developed a novel heterogeneous parallel implementation of the ALC-PSO algorithm using OmpSs and CUDA, for execution on both CPU and GPU cores. OmpSs is a task-based parallel programming model that uses runtime system and hardware for automatic exploiting and managing parallelisms. Combination of OmpSs with CUDA helps developers accelerate their applications running on both CPUs and GPUs. The results demonstrate that the proposed approach (i.e., the OmpSs-CUDA implementation of the ALC-PSO algorithm) provides higher performance than the serial, and also the CUDA-based parallel implementations of the ALC-PSO algorithm. Parallel programming Particle Swarm Optimization ALC-PSO OmpSs CUDA 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. 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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-2292185","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":153548097,"identity":"3f1d55d9-2f24-4164-9bb7-622a3e1ee303","order_by":0,"name":"Fahimeh Yazdanpanah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYJACCQjFw/DhA0wogUgtjDNnkKxlNg8xjjI4fvzhjZ877sibs5892Gzbdi+xgf3wA4aHe/BoOZOQbNl75pnhzp68xObctuLEBp40A4aEZ3i0HEg4JsHbdphxw4Ec88c5ZxISGxhygH45gEfL+Ydtkn/bDttvOP/GsNkCpIX/DQEtN5LZpIG2JG64kWPYzFAB1CJBwBbJG8+YrWXbniVvuPHGsLGnIsG4TeIZ0LV4tPCdT394823bHdsN53MMG34YJMj28yc/fPgDjxYFiBySCjZULiaQb0DXMgpGwSgYBaMAHQAADw5c+Kt5wh4AAAAASUVORK5CYII=","orcid":"","institution":"Vali-e-Asr University of Rafsanjan","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Fahimeh","middleName":"","lastName":"Yazdanpanah","suffix":""}],"badges":[],"createdAt":"2022-11-19 20:29:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2292185/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2292185/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":30288441,"identity":"1b864581-af8c-4eff-ad51-4d78f06fb2e2","added_by":"auto","created_at":"2022-12-13 23:29:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":473943,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2292185/v1_covered.pdf"},{"id":29426046,"identity":"7a422e68-278e-4ddf-aea8-cf1e5a3cb802","added_by":"auto","created_at":"2022-11-23 07:35:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":468264,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2292185/v1_covered.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Heterogeneous Parallel Implementation of PSO Algorithm with an Aging Leader and Challengers","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":"Parallel programming, Particle Swarm Optimization, ALC-PSO, OmpSs, CUDA","lastPublishedDoi":"10.21203/rs.3.rs-2292185/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2292185/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eParallel computing techniques provide high-performance execution of large-size and real-world complicated problems such as heuristic optimization algorithms. 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