An Improved Snow Ablation Optimizer Based on Multi-Strategy Fusion for Global Optimization Problems

An Improved Snow Ablation Optimizer Based on Multi-Strategy Fusion for Global Optimization Problems | 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 An Improved Snow Ablation Optimizer Based on Multi-Strategy Fusion for Global Optimization Problems Lulu Zheng, Yadong Wang, Xuefeng Deng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6878113/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 The Snow Ablation Optimizer (SAO) is a novel meta-heuristic search algorithm proposed in 2023, which solves complex optimization problems by simulating snow's melting and sublimation processes. However, SAO experiences slow convergence rates and is prone to getting stuck in local optima. which limits its performance. To address these issues, an Improved Snow Ablation Optimizer (ISAO) is proposed in this paper. First, Latin Hypercube Sampling (LHS) is used in the population initialization stage, which significantly improves the diversity of the initial population and the coverage of the search space. Second, the exploration phase incorporates the Whale Optimization Algorithm (WOA) with combined encircling attack and spiral update strategies, enhancing global search capability and effectively avoiding local optima. Finally, Variable Neighborhood Search (VNS) is introduced in the exploitation phase, and three neighborhood structures, namely, micro-perturbation, strong-perturbation, and jump-perturbation, are designed to achieve a more flexible and refined local search. To validate the performance of ISAO, this paper conducts experimental evaluations on the CEC2017 benchmark test functions, including comparative analyses with the original algorithm and its three improved modules, as well as performance comparisons with five classical optimization algorithms. In addition, the Wilcoxon rank sum test was employed to verify the significance of ISAO. Experimental results show that ISAO significantly outperforms the original and comparison algorithms regarding convergence speed, solution accuracy, and robustness, demonstrating excellent optimization performance. Improved snow ablation optimizer Variable neighborhood search Whale optimization algorithm(WOA) Latin hypercube sampling (LHS) 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-6878113","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":495358455,"identity":"85d9f403-4649-4f05-8678-55f59b37b601","order_by":0,"name":"Lulu Zheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIie3RPUsDMRjA8ecIxCVw63MI9StEAlLxbL9KQiCjCC6OAeHc2lXBD3FQcE4N6FLoGtDBW5wcLIJTEaOTDjludMh/yBDyI28Audw/rCTEd5vPmpW/Z7GPVJeNEUjNqLJkIOGrFUdG7wR3QwkEyaNxarFe3yNs/V60y0cGk5OUKK6kfD7FJ3UbNMWiMfuto/qIgT5LEYLSceQvkZBIbF20jh3sMnDKJghFZZFJrxZzT+PBcNq68qOXMOYhXsULDvFgQGsVd6G9BHcaKiprRhi0GKvG6GtPxeEN10ky9eV7t7HxK+fLLrxt/fHs4aILr+eTJPmb/HmR74EPWp/L5XK5RF/yL1W/y42RzQAAAABJRU5ErkJggg==","orcid":"","institution":"Shanxi Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Lulu","middleName":"","lastName":"Zheng","suffix":""},{"id":495358456,"identity":"6512a794-6afa-4d75-93ea-116dfcfee850","order_by":1,"name":"Yadong Wang","email":"","orcid":"","institution":"Shanxi Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yadong","middleName":"","lastName":"Wang","suffix":""},{"id":495358457,"identity":"8a5d4dc7-5765-4d76-ad5f-557abef142ad","order_by":2,"name":"Xuefeng Deng","email":"","orcid":"","institution":"Shanxi Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Xuefeng","middleName":"","lastName":"Deng","suffix":""}],"badges":[],"createdAt":"2025-06-12 08:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6878113/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6878113/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104401755,"identity":"806e8bd6-0169-45dc-bd34-fa6dfa7e6605","added_by":"auto","created_at":"2026-03-11 12:13:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9541575,"visible":true,"origin":"","legend":"","description":"","filename":"ClusterComputing.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6878113/v1_covered_d8f40f81-0683-4cc8-a1fb-949b7e62bd00.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Improved Snow Ablation Optimizer Based on Multi-Strategy Fusion for Global Optimization Problems","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":"Improved snow ablation optimizer, Variable neighborhood search, Whale optimization algorithm(WOA), Latin hypercube sampling (LHS)","lastPublishedDoi":"10.21203/rs.3.rs-6878113/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6878113/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The Snow Ablation Optimizer (SAO) is a novel meta-heuristic search algorithm proposed in 2023, which solves complex optimization problems by simulating snow's melting and sublimation processes. However, SAO experiences slow convergence rates and is prone to getting stuck in local optima. which limits its performance. To address these issues, an Improved Snow Ablation Optimizer (ISAO) is proposed in this paper. First, Latin Hypercube Sampling (LHS) is used in the population initialization stage, which significantly improves the diversity of the initial population and the coverage of the search space. Second, the exploration phase incorporates the Whale Optimization Algorithm (WOA) with combined encircling attack and spiral update strategies, enhancing global search capability and effectively avoiding local optima. Finally, Variable Neighborhood Search (VNS) is introduced in the exploitation phase, and three neighborhood structures, namely, micro-perturbation, strong-perturbation, and jump-perturbation, are designed to achieve a more flexible and refined local search. To validate the performance of ISAO, this paper conducts experimental evaluations on the CEC2017 benchmark test functions, including comparative analyses with the original algorithm and its three improved modules, as well as performance comparisons with five classical optimization algorithms. In addition, the Wilcoxon rank sum test was employed to verify the significance of ISAO. 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