Quantum Based Buzzard Optimization Algorithm

Quantum Based Buzzard Optimization Algorithm | 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 Quantum Based Buzzard Optimization Algorithm Ali Arshaghi, Mohsen Norouzi, navid razmjooy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2060456/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 paper proposes an evolutionary computing method called quantum buzzard optimization algorithm (QBUZO). The QBUZO algorithm is a modification of the BUZO algorithm based on quantum system. In quantum, the particle can be represented by its instantaneous motion and the energy of its wave function. In QBUZO, each particle is defined with quantum state in this system and is established with a wave function instead of the position and capability vector in BUZO algorithm. The idea is to allow all particles to have quantum behavior instead of the classical Newtonian random motion. We present the individual particle of a BUZO system moving in quantum multidimensional space, and create a quantum delta potential well model for BUZO. The effectiveness of QBUZO is demonstrated by experimental results on some benchmark mathematical functions in optimization. The comparison of performances with six other optimization algorithms represents that QBUZO is superior to other algorithms. Quantum system Optimization Buzzard Optimization Meta-heuristic Particle swarm optimization Computational intelligence 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-2060456","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":154813038,"identity":"099b54d8-34d9-4cf1-837e-84e321e09aec","order_by":0,"name":"Ali Arshaghi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIie3RMQrCMBSA4RcCdUlw7WSvkNJV26tUegIRXBwsFNLRteIlCg7qFniDi+gVhIKz0qmbKYpubd0E8y8hIR95EACT6VcjMYCgVOFn241Y4bcEmKCdZurH9Hqvdr6/7bE7TiQ4+kTemoitLG/Nj1G0T3iOKwlupkiSNT6jwKNE0kigJlwCyYEkjcJRvZJUcqEJu9QkaCVCMQ+4RF8TqMm4lbjIppTLQyjQ0upkRxm2kMEh3ejB5oE4Y1Gy2XC0TNOikcDrL8bxc7XfJ60FHe+ZTCbTP/YA7mxEr3hrssQAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-9008-249X","institution":"Imam Hossein University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Arshaghi","suffix":""},{"id":154813039,"identity":"3a64937a-aaab-41ac-8094-5d045da8e0f2","order_by":1,"name":"Mohsen Norouzi","email":"","orcid":"","institution":"Imam Hossein University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mohsen","middleName":"","lastName":"Norouzi","suffix":""},{"id":154813040,"identity":"3abeb52f-086a-4cf2-a948-ce174c0f42d1","order_by":2,"name":"navid razmjooy","email":"","orcid":"","institution":"Islamic Azad University Ardabil Branch","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"navid","middleName":"","lastName":"razmjooy","suffix":""}],"badges":[],"createdAt":"2022-09-13 10:59:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2060456/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2060456/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":36484728,"identity":"48acdaaa-bdc0-420d-a5af-bc62cddcfef4","added_by":"auto","created_at":"2023-05-01 03:30:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":547226,"visible":true,"origin":"","legend":"","description":"","filename":"QuantumBasedBuzzardOptimizationAlgorithmarshaghi.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2060456/v1_covered_05ef85cc-c31c-446d-8c42-91a3f26acfaa.pdf"}],"financialInterests":"","formattedTitle":"Quantum Based Buzzard Optimization Algorithm","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":"Quantum system, Optimization, Buzzard Optimization, Meta-heuristic, Particle swarm optimization, Computational intelligence","lastPublishedDoi":"10.21203/rs.3.rs-2060456/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2060456/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper proposes an evolutionary computing method called quantum buzzard optimization algorithm (QBUZO). 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