Optimization of SVM Parameters using Gaussian Quantum-Behaved PSO

Optimization of SVM Parameters using Gaussian Quantum-Behaved PSO | 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Article Optimization of SVM Parameters using Gaussian Quantum-Behaved PSO Amit Kumar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7019787/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 This research seeks to offer insights regarding optimizing support vector machines (SVM) in terms of their parameters which are critical for streamlining accurate classifications for different tasks. We introduce a novel optimization approach called Gaussian quantum-behaved particle swarm optimization (GQPSO), denoted GQPSO-SVM, that is meant to minimize the test error rate very efficiently by finding the optimal SVM parameters. A number of experiments were conducted to compare the GQPSO-SVM algorithm with wellknown techniques including BA+SVM, PSO+SVM, and QPSO+SVM. The results show that GQPSO-SVM regularly achieves lower test error rates than its alternatives with the radial basis function (RBF) kernel performing better than the polynomial kernel. This illustrates how GQPSO-SVM can improve SVM classifier performance and dependability across a range of applications. Support Vector Machine Classifier Parameter Optimization Guassian Quantum- behaved Particle Swarm Optimization Full Text 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. 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-7019787","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":562834481,"identity":"06adf432-8517-4397-b087-c31d6c3c1fa3","order_by":0,"name":"Amit Kumar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFklEQVRIiWNgGAWjYHACZijN2MDwwUBCjh/ETiggTktj44wKG2PJBpAWA6K0MDA285xJS9xwAMTGo4Vf+gCzMW+OXT7/tMPtD3jbDiduPr868cMDAwZ5frEDWLVI9iUwJ/NuS7accTuxsUGy7bDxthtvN0sAHWY4c3YCVi0GZxiYD/NuYzZgAGkxbDssu+3G2Q0gLQkGt7FrsYdoqTeQB2lJbDvMuHnG2c0/8Gkx4GEAOeywgQFIy4EzaYob+Hu34bVF4gxjs+HcbccNDIFaZjYAA1niBu82iwQDCZx+4e9hPizxdlu1gdzt9Aef/4Cisv/s5ps/Kmzk+aWxawFHOprFYJUSOJRjt/gAKapHwSgYBaNgBAAAu/Vjzf+F9AIAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-8594-4515","institution":"Rajkiya Engineering College Ambedkar Nagar","correspondingAuthor":true,"prefix":"","firstName":"Amit","middleName":"","lastName":"Kumar","suffix":""}],"badges":[],"createdAt":"2025-07-01 11:31:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7019787/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7019787/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98739720,"identity":"f72acaef-5e98-4f98-9a48-4e2a326919fc","added_by":"auto","created_at":"2025-12-22 07:30:34","extension":"xml","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5799,"visible":true,"origin":"","legend":"","description":"","filename":"ijsaIJSAD2501663.xml","url":"https://assets-eu.researchsquare.com/files/rs-7019787/v1/99825897c057c33b74ba022c.xml"},{"id":98739722,"identity":"73862198-2b48-402b-9028-e8cfd9f2f82c","added_by":"auto","created_at":"2025-12-22 07:30:34","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":957,"visible":true,"origin":"","legend":"","description":"","filename":"IJSAD250166330367.go.xml","url":"https://assets-eu.researchsquare.com/files/rs-7019787/v1/d10b77c66a02e11b09d5403f.xml"},{"id":98739719,"identity":"3370307f-98a9-489f-a9c5-1c1354ac45ff","added_by":"auto","created_at":"2025-12-22 07:30:34","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":888,"visible":true,"origin":"","legend":"","description":"","filename":"IJSAD2501663Import.xml","url":"https://assets-eu.researchsquare.com/files/rs-7019787/v1/b477994e0763187fddca264f.xml"},{"id":104399670,"identity":"148504ab-49e0-45cf-9744-c49af84d06dc","added_by":"auto","created_at":"2026-03-11 12:07:11","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":498658,"visible":true,"origin":"","legend":"","description":"","filename":"OptimizationofSVMParametersusingGQPSO.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7019787/v1_covered_53260d67-96c9-491b-9159-1d4f65fa0c50.pdf"}],"financialInterests":"","formattedTitle":"Optimization of SVM Parameters using Gaussian Quantum-Behaved PSO","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":"Support Vector Machine Classifier, Parameter Optimization, Guassian Quantum- behaved Particle Swarm Optimization","lastPublishedDoi":"10.21203/rs.3.rs-7019787/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7019787/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This research seeks to offer insights regarding optimizing support vector machines (SVM) in terms of their parameters which are critical for streamlining accurate classifications for different tasks. 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