Weather State Ants Optimizer (WSAO): A Novel Dynamic Variable Structure Metaheuristic Algorithm Incorporating Markov Processes

Weather State Ants Optimizer (WSAO): A Novel Dynamic Variable Structure Metaheuristic Algorithm Incorporating Markov Processes | 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 Weather State Ants Optimizer (WSAO): A Novel Dynamic Variable Structure Metaheuristic Algorithm Incorporating Markov Processes Xiubo Xia, Yongling Fu, Donghao Jia, Pu Zhang, Jian Sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8958426/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract The performance of metaheuristic algorithms in solving complex optimization problems critically depends on an effective balance between exploration and exploitation. While existing variable-structure algorithms attempt to address this challenge through predefined stage-wise switching strategies, their rigid transition rules often limit adaptability in complex search landscapes, leading to issues such as slow convergence or premature convergence to local optima. To overcome these limitations, this paper proposes the Weather State Ants Optimizer (WSAO), a novel dynamic variable structure metaheuristic algorithm that simulates the intelligent foraging behavior of ants under varying weather conditions. The primary innovation lies in the introduction of a Markov process-driven dynamic weather state machine. At each generation, the algorithm transitions between three distinct states according to the Markov process, each governing a fundamentally different optimization structure dedicated to either exploration or exploitation. This design enables probabilistic and memoryless switching of search modes throughout the optimization process, achieving real-time dynamic adjustment of the exploration–exploitation trade-off. A second key innovation is the incorporation of a dynamic nest mechanism, where elite solutions establish multiple search centers, enabling concurrent exploitation of multiple promising regions and substantially improving performance on multimodal problems. To validate the superiority of WSAO, we conducted a comprehensive comparison with 11 state-of-the-art algorithms on CEC2017 and CEC2022 benchmark functions, as well as 5 constrained engineering problems. Statistical results show that WSAO achieves leading or highly competitive results in a clear majority of test cases, particularly demonstrating clear advantages in complex multimodal and hybrid functions. This work not only presents a powerful optimizer but, more importantly, pioneers the integration of Markov processes into metaheuristic algorithms, establishing a new paradigm for dynamic variable-structure optimization with significant implications for the field. The source code and test results of the WSAO algorithm is available at https://github.com/xiaxiubo/WSAO-Weather-State-Ants-Optimizer . Weather State Ants Optimizer (WSAO) Metaheuristic Algorithm Dynamic Variable Structure Algorithm Optimization Markov Processes Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 30 Mar, 2026 Reviewers agreed at journal 29 Mar, 2026 Reviewers invited by journal 29 Mar, 2026 Editor assigned by journal 13 Mar, 2026 Submission checks completed at journal 26 Feb, 2026 First submitted to journal 24 Feb, 2026 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-8958426","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":614255840,"identity":"6ce26aa2-3bbe-4390-85d7-ea3e6f173ae1","order_by":0,"name":"Xiubo Xia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYDACZiBOYDjAwC8B4TM2EK1FcgbRWiDgAIPBDWK1GBxnfibxsO2OnPHtHsMPPxhsZDccYH72AJ8WyWY2Y4PEtmfGZnfOGEv2MKQZbzjAZm6ATws/M4Phg8S2w4nbbuQYSPAwHE7ccICHTQKfFjZm9g8HQFo2z8gx/vmH4T9hLfzMPBBbNkjkmEnzMBwgrEWymafYIOHcM2OJO8fKrGUMko1nHmYzw6vF4PzxbZI/yu7I8c9u3nzzTYWdbN/x5md4taCbwACJ3FEwCkbBKBgFlAEADShJqmGZuQ4AAAAASUVORK5CYII=","orcid":"","institution":"Beihang University","correspondingAuthor":true,"prefix":"","firstName":"Xiubo","middleName":"","lastName":"Xia","suffix":""},{"id":614255841,"identity":"fd8a6f34-bd10-4e8a-8c18-ed373d85cfca","order_by":1,"name":"Yongling Fu","email":"","orcid":"","institution":"Beihang University","correspondingAuthor":false,"prefix":"","firstName":"Yongling","middleName":"","lastName":"Fu","suffix":""},{"id":614255845,"identity":"2b4d5b18-1999-41eb-a405-ae86478c9275","order_by":2,"name":"Donghao Jia","email":"","orcid":"","institution":"Beihang University","correspondingAuthor":false,"prefix":"","firstName":"Donghao","middleName":"","lastName":"Jia","suffix":""},{"id":614255846,"identity":"89646eed-c8e4-45c8-adb6-68d577cbf83e","order_by":3,"name":"Pu Zhang","email":"","orcid":"","institution":"Beihang University","correspondingAuthor":false,"prefix":"","firstName":"Pu","middleName":"","lastName":"Zhang","suffix":""},{"id":614255849,"identity":"6d00406b-e587-4e50-9716-8548cad14692","order_by":4,"name":"Jian Sun","email":"","orcid":"","institution":"Beihang University","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2026-02-24 14:10:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8958426/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8958426/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106093890,"identity":"fa12ed77-4f2a-471f-a6e6-9819c41db009","added_by":"auto","created_at":"2026-04-03 11:39:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1961299,"visible":true,"origin":"","legend":"","description":"","filename":"WeatherStateAntsOptimizerWSAO.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8958426/v1_covered_7d59c22a-3c02-4cfd-b8fe-54223e47a70a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Weather State Ants Optimizer (WSAO): A Novel Dynamic Variable Structure Metaheuristic Algorithm Incorporating Markov Processes","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"cluster-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Cluster Computing](https://www.springer.com/journal/10586)","snPcode":"10586","submissionUrl":"https://submission.nature.com/new-submission/10586/3","title":"Cluster Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Weather State Ants Optimizer (WSAO), Metaheuristic Algorithm, Dynamic Variable Structure Algorithm, Optimization, Markov Processes","lastPublishedDoi":"10.21203/rs.3.rs-8958426/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8958426/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe performance of metaheuristic algorithms in solving complex optimization problems critically depends on an effective balance between exploration and exploitation. While existing variable-structure algorithms attempt to address this challenge through predefined stage-wise switching strategies, their rigid transition rules often limit adaptability in complex search landscapes, leading to issues such as slow convergence or premature convergence to local optima. To overcome these limitations, this paper proposes the Weather State Ants Optimizer (WSAO), a novel dynamic variable structure metaheuristic algorithm that simulates the intelligent foraging behavior of ants under varying weather conditions. The primary innovation lies in the introduction of a Markov process-driven dynamic weather state machine. At each generation, the algorithm transitions between three distinct states according to the Markov process, each governing a fundamentally different optimization structure dedicated to either exploration or exploitation. This design enables probabilistic and memoryless switching of search modes throughout the optimization process, achieving real-time dynamic adjustment of the exploration\u0026ndash;exploitation trade-off. A second key innovation is the incorporation of a dynamic nest mechanism, where elite solutions establish multiple search centers, enabling concurrent exploitation of multiple promising regions and substantially improving performance on multimodal problems. To validate the superiority of WSAO, we conducted a comprehensive comparison with 11 state-of-the-art algorithms on CEC2017 and CEC2022 benchmark functions, as well as 5 constrained engineering problems. Statistical results show that WSAO achieves leading or highly competitive results in a clear majority of test cases, particularly demonstrating clear advantages in complex multimodal and hybrid functions. This work not only presents a powerful optimizer but, more importantly, pioneers the integration of Markov processes into metaheuristic algorithms, establishing a new paradigm for dynamic variable-structure optimization with significant implications for the field. 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