A hybrid optimization algorithm of differential evolution and sine cosine algorithm

A hybrid optimization algorithm of differential evolution and sine cosine 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 A hybrid optimization algorithm of differential evolution and sine cosine algorithm Congqian Wang, Shasha Wang, Zechen Zheng, Chao Fan, Miao Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5021379/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 solves the shortcomings of the sine cosine algorithm (SCA) when applied to optimizing high-dimensional functions. This paper presents a hybrid optimization algorithm of differential evolution and sine cosine algorithm (DESCA), which combined the advantages of DE and SCA, and applied the spiral update in the WOA. Firstly, the same parameters r 2 , r 3 , and r 4 are used to improve exploration and accelerate the convergence speed of the SCA. Secondly, the spiral update strategy of the whale optimization algorithm (WOA) is applied to update the SCA results, enhancing its exploitation. Finally, the SCA is implemented in the early stage of the DESCA, and the DE with DE/best/1 variation strategy is implemented in the late stage of the DESCA, which realizes the complementary advantages of the two algorithms and the exploration and exploitation of DESCA are well balanced. Simulation experiments on 23 benchmark functions, CEC 2014 and CEC 2020 illustrate that DESCA has higher optimization performance. In addition, two mechanical optimization problems are solved through the DESCA, which proves its practicability. Sine cosine algorithm Differential evolution Whale optimization algorithm Spiral update Mechanical optimization design 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. 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-5021379","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":353924615,"identity":"04e0bb91-2570-4eae-85c8-6ec961ed7c43","order_by":0,"name":"Congqian Wang","email":"","orcid":"","institution":"Northwest University","correspondingAuthor":false,"prefix":"","firstName":"Congqian","middleName":"","lastName":"Wang","suffix":""},{"id":353924616,"identity":"2b0bfb65-ea05-482c-8c9d-21650f07cc80","order_by":1,"name":"Shasha Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAm0lEQVRIiWNgGAWjYHACxgcQOoF4LcwGJGthkyBNi8H548+qC9sOM/Cz5xgw/NxBjJYbOWa3ZwK1SPa8MWDsPUOEFrMbPGy3ebcdBuk1YGZsI0YL0GHFIC32xGs5kGDGDLZFglgtQMONpXn/pfNInHlWcLCXGC2S/ccffuY5Yy3H35688cFPYrTAAA+IOECChlEwCkbBKBgF+AAApVky2cGWvcUAAAAASUVORK5CYII=","orcid":"","institution":"Northwest University","correspondingAuthor":true,"prefix":"","firstName":"Shasha","middleName":"","lastName":"Wang","suffix":""},{"id":353924617,"identity":"2c76e989-88d3-4545-990f-9201d91d3d21","order_by":2,"name":"Zechen Zheng","email":"","orcid":"","institution":"Northwest University","correspondingAuthor":false,"prefix":"","firstName":"Zechen","middleName":"","lastName":"Zheng","suffix":""},{"id":353924618,"identity":"e1b38ef8-c423-4e06-ab2c-9bc718814b94","order_by":3,"name":"Chao Fan","email":"","orcid":"","institution":"Northwest University","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Fan","suffix":""},{"id":353924619,"identity":"0f98aa79-9d71-4771-b075-2d83e83d9d78","order_by":4,"name":"Miao Wang","email":"","orcid":"","institution":"Northwest University","correspondingAuthor":false,"prefix":"","firstName":"Miao","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-09-03 02:49:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5021379/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5021379/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81079348,"identity":"b5571e80-c013-456f-873c-70d1fa59ab32","added_by":"auto","created_at":"2025-04-22 03:53:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":528694,"visible":true,"origin":"","legend":"","description":"","filename":"Ahybridoptimizationalgorithmofdifferentialevolutionandsinecosinealgorithm.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5021379/v1_covered_da15d9b5-b694-4c7e-9cfb-4852cfd63daa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A hybrid optimization algorithm of differential evolution and sine cosine 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":"Sine cosine algorithm, Differential evolution, Whale optimization algorithm, Spiral update, Mechanical optimization design","lastPublishedDoi":"10.21203/rs.3.rs-5021379/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5021379/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper solves the shortcomings of the sine cosine algorithm (SCA) when applied to optimizing high-dimensional functions. This paper presents a hybrid optimization algorithm of differential evolution and sine cosine algorithm (DESCA), which combined the advantages of DE and SCA, and applied the spiral update in the WOA. Firstly, the same parameters r\u003csub\u003e2\u003c/sub\u003e , r\u003csub\u003e3\u003c/sub\u003e , and r\u003csub\u003e4 \u003c/sub\u003eare used to improve exploration and accelerate the convergence speed of the SCA. Secondly, the spiral update strategy of the whale optimization algorithm (WOA) is applied to update the SCA results, enhancing its exploitation. Finally, the SCA is implemented in the early stage of the DESCA, and the DE with DE/best/1 variation strategy is implemented in the late stage of the DESCA, which realizes the complementary advantages of the two algorithms and the exploration and exploitation of DESCA are well balanced. Simulation experiments on 23 benchmark functions, CEC 2014 and CEC 2020 illustrate that DESCA has higher optimization performance. 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