Improved Multi-objective Particle Swarm Algorithm Combined with North Goshawk Optimization Hyperparametric Optimization Least Squares Support Vector Machines for Linear Motors

Improved Multi-objective Particle Swarm Algorithm Combined with North Goshawk Optimization Hyperparametric Optimization Least Squares Support Vector Machines for Linear Motors | 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 Multi-objective Particle Swarm Algorithm Combined with North Goshawk Optimization Hyperparametric Optimization Least Squares Support Vector Machines for Linear Motors Cheng Wen, Jian Cui, Mingye Li, Xuekai Zhu, Junyi Chen, Aosai Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5740193/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract In this study, we propose a Least Squares Support Vector Machine (LSSVM) combining the hyperparametric optimization of the Northern Goshawk algorithm (NGO) with an improved Multi-Objective Particle Swarm Optimization (MOPSO) Algorithm for the optimization of linear motors. Meanwhile, in order to improve the performance of the MOPSO algorithm in terms of population diversity, convergence speed and local search ability, we incorporate chaotic mapping and dynamic weighting mechanism. With this approach, the performance of linear motors can be predicted more accurately and their design parameters can be optimized. Firstly, a finite element simulation model of the motor is constructed, and analytical models for thrust force and thrust fluctuation are theoretically derived. Secondly, the Plackett-Burman design is employed to screen key factors and establish an experimental design space, reducing unnecessary variables and improving optimization efficiency. Subsequently, predictive regression modeling of the experimental space is performed using LSSVM, with hyperparameters optimized by NGO to enhance model performance. Finally, an iterative optimization of the combined model is conducted using the improved MOPSO to identify optimal structural parameter configurations. The optimized results are verified through finite element analysis (FEA), confirming the effectiveness and accuracy of the proposed method in enhancing motor performance Physical sciences/Engineering Physical sciences/Engineering/Electrical and electronic engineering Machine Learning Linear Motor NGO LSSVM MOPSO FEA Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 15 Apr, 2025 Reviews received at journal 14 Apr, 2025 Reviews received at journal 10 Apr, 2025 Reviewers agreed at journal 07 Apr, 2025 Reviewers agreed at journal 05 Apr, 2025 Reviewers invited by journal 05 Apr, 2025 Submission checks completed at journal 03 Apr, 2025 First submitted to journal 28 Mar, 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. 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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-5740193","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":439767227,"identity":"bf5b35ec-efd1-477f-8a1e-2381f5f97078","order_by":0,"name":"Cheng Wen","email":"","orcid":"","institution":"Hebei Provincial Collaborative Innovation Center of Transportation Power Grid Intelligent Integration Technology and Equipment","correspondingAuthor":false,"prefix":"","firstName":"Cheng","middleName":"","lastName":"Wen","suffix":""},{"id":439767228,"identity":"8b2fab44-1efc-47a4-9b61-907280737b44","order_by":1,"name":"Jian 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Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Machine Learning, Linear Motor, NGO, LSSVM, MOPSO, FEA","lastPublishedDoi":"10.21203/rs.3.rs-5740193/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5740193/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this study, we propose a Least Squares Support Vector Machine (LSSVM) combining the hyperparametric optimization of the Northern Goshawk algorithm (NGO) with an improved Multi-Objective Particle Swarm Optimization (MOPSO) Algorithm for the optimization of linear motors. Meanwhile, in order to improve the performance of the MOPSO algorithm in terms of population diversity, convergence speed and local search ability, we incorporate chaotic mapping and dynamic weighting mechanism. With this approach, the performance of linear motors can be predicted more accurately and their design parameters can be optimized. Firstly, a finite element simulation model of the motor is constructed, and analytical models for thrust force and thrust fluctuation are theoretically derived. Secondly, the Plackett-Burman design is employed to screen key factors and establish an experimental design space, reducing unnecessary variables and improving optimization efficiency. Subsequently, predictive regression modeling of the experimental space is performed using LSSVM, with hyperparameters optimized by NGO to enhance model performance. Finally, an iterative optimization of the combined model is conducted using the improved MOPSO to identify optimal structural parameter configurations. 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