Improved Black-winged Kite Optimization Algorithm with Multi-Strategy Hybrid and Its Application

Improved Black-winged Kite Optimization Algorithm with Multi-Strategy Hybrid and Its Application | 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 Article Improved Black-winged Kite Optimization Algorithm with Multi-Strategy Hybrid and Its Application Lichuan Hui, Yixiang Kong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7825101/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract This paper aims at the problems of low initial population diversity and being prone to fall into local optimum in the basic Black-winged kite optimization algorithm (BKA) and proposes an improved multi-strategy hybrid black-winged kite optimization algorithm (IMBKA) and its application. Firstly, in the process of generating the initial group, the optimal point set model is adopted for optimization; Secondly, an adaptive weighting method has been added to the attack behavior; Then, alert behaviors that can significantly enhance the robustness and optimize the performance of the algorithm were introduced; Finally, the Levy flight strategy was combined with the migration behavior to prevent the algorithm from becoming trapped in a local optimum. In this study, the Markov chain was constructed to prove the convergence of the improved algorithm, and a test function was used to conduct a comparative test of IMBKA with five other algorithms. The results demonstrate that the performance of the improved algorithm surpasses that of the other algorithms. In practical applications, a model for predicting pantograph-catenary contact resistance is constructed by optimizing the parameters of the Support Vector Machine (SVM) through IMBKA. The model’s prediction results further demonstrate that the improved algorithm is practical. Physical sciences/Engineering Physical sciences/Mathematics and computing black-winged kite algorithm optimal point set model Adaptive inertia weight Spiral search strategy Markov chain Contact resistance prediction Full Text Additional Declarations No competing interests reported. Supplementary Files IMBKASVM.rar AlgorithmComparison.rar Cite Share Download PDF Status: Published Journal Publication published 30 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 09 Dec, 2025 Reviews received at journal 09 Dec, 2025 Reviewers agreed at journal 04 Dec, 2025 Reviewers agreed at journal 04 Dec, 2025 Reviews received at journal 05 Nov, 2025 Reviewers agreed at journal 20 Oct, 2025 Reviewers invited by journal 20 Oct, 2025 Editor assigned by journal 20 Oct, 2025 Editor invited by journal 20 Oct, 2025 Submission checks completed at journal 19 Oct, 2025 First submitted to journal 19 Oct, 2025 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-7825101","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":532718648,"identity":"1d6a1337-49ae-4be3-9ea1-66e5ac0a65ca","order_by":0,"name":"Lichuan Hui","email":"","orcid":"","institution":"Liaoning Technical University","correspondingAuthor":false,"prefix":"","firstName":"Lichuan","middleName":"","lastName":"Hui","suffix":""},{"id":532718649,"identity":"ac8f4030-bab6-47e4-96cf-0dee8229476b","order_by":1,"name":"Yixiang 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optimal point set model, Adaptive inertia weight, Spiral search strategy, Markov chain, Contact resistance prediction","lastPublishedDoi":"10.21203/rs.3.rs-7825101/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7825101/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper aims at the problems of low initial population diversity and being prone to fall into local optimum in the basic Black-winged kite optimization algorithm (BKA) and proposes an improved multi-strategy hybrid black-winged kite optimization algorithm (IMBKA) and its application. Firstly, in the process of generating the initial group, the optimal point set model is adopted for optimization; Secondly, an adaptive weighting method has been added to the attack behavior; Then, alert behaviors that can significantly enhance the robustness and optimize the performance of the algorithm were introduced; Finally, the Levy flight strategy was combined with the migration behavior to prevent the algorithm from becoming trapped in a local optimum. In this study, the Markov chain was constructed to prove the convergence of the improved algorithm, and a test function was used to conduct a comparative test of IMBKA with five other algorithms. The results demonstrate that the performance of the improved algorithm surpasses that of the other algorithms. In practical applications, a model for predicting pantograph-catenary contact resistance is constructed by optimizing the parameters of the Support Vector Machine (SVM) through IMBKA. 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