Cloud Drift Optimization (CDO) Algorithm: A Nature-Inspired Metaheuristic

Cloud Drift Optimization (CDO) Algorithm: A Nature-Inspired Metaheuristic | 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 Cloud Drift Optimization (CDO) Algorithm: A Nature-Inspired Metaheuristic Mohammad Alibabaei Shahraki This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6430604/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 study introduces the Cloud Drift Optimization (CDO) algorithm, an innovative nature-inspired metaheuristic approach to solving complex optimization problems. The CDO algorithm mimics the dynamic behavior of cloud particles influenced by atmospheric forces, striking a refined balance between exploration and exploitation. It features an adaptive weight adjustment mechanism that alters the cloud's drift behavior in real-time, allowing for efficient navigation through the search space. Using a cloud-based drift strategy, CDO harnesses probabilistic movements to maneuver through the optimization landscape more effectively. The algorithm has undergone rigorous testing against various established unimodal and multimodal benchmark functions, where it showcases outstanding performance characterized by faster convergence rates, high robustness, and exceptional solution accuracy compared to top contemporary optimization techniques. Additionally, CDO applies to numerous real-world engineering optimization tasks, such as designing cantilever beams, three-bar trusses, tension/compression springs, and pressure vessels. The empirical data highlight CDO's ability to deliver innovative solutions across engineering fields, machine learning applications, and other practical optimization scenarios. These results indicate that CDO is a promising tool for tackling highly complex and multidimensional problems in academic and industrial environments. Optimization Optimization techniques Metaheuristic Algorithms Constrained optimization Algorithm Full Text Additional Declarations The authors declare no competing interests. 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-6430604","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":441836649,"identity":"4e6f7eb3-3af8-4089-a51a-360a3d7358ea","order_by":0,"name":"Mohammad Alibabaei Shahraki","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-0707-7735","institution":"National University of Skills","correspondingAuthor":true,"prefix":"","firstName":"Mohammad","middleName":"Alibabaei","lastName":"Shahraki","suffix":""}],"badges":[],"createdAt":"2025-04-11 18:45:36","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6430604/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6430604/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80504153,"identity":"b0cc486e-9621-4c92-8a27-2e4b79fd4be7","added_by":"auto","created_at":"2025-04-14 04:25:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1645756,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6430604/v1_covered_8a7f90db-4e24-4b96-a656-f576b05bfa77.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eCloud Drift Optimization (CDO) Algorithm: A Nature-Inspired Metaheuristic\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"National University of Skills","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"Optimization, Optimization techniques, Metaheuristic Algorithms, Constrained optimization, Algorithm","lastPublishedDoi":"10.21203/rs.3.rs-6430604/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6430604/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study introduces the Cloud Drift Optimization (CDO) algorithm, an innovative nature-inspired metaheuristic approach to solving complex optimization problems. 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