Hybrid rice optimization algorithm inspired grey wolf optimizer for high-dimensional feature selection

Hybrid rice optimization algorithm inspired grey wolf optimizer for high-dimensional feature selection | 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 Hybrid rice optimization algorithm inspired grey wolf optimizer for high-dimensional feature selection Zhiwei Ye, Ruoxuan Huang, Wen Zhou, Mingwei Wang, Ting Cai, Qiyi He, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4598290/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Dec, 2024 Read the published version in Scientific Reports → Version 1 posted 17 You are reading this latest preprint version Abstract Feature selection (FS) is a significant dimensionality reduction technique, which can effectively remove redundant features. Metaheuristic algorithms have been widely employed in FS, and have obtained satisfactory performance, among them, grey wolf optimizer (GWO) has received widespread attention. However, the GWO and its variants suffer from limited adaptability, poor diversity, and low accuracy when faced with high-dimensional data. The hybrid rice optimization (HRO) algorithm is an emerging metaheuristic algorithm derived from the hybrid heterosis and breeding mechanism in nature. It possesses a robust capacity to identify and converge towards optimal solutions. Therefore, a novel approach based on multi-strategy collaborative GWO combined with the HRO algorithm (HRO-GWO) for FS is proposed in this paper. The HRO-GWO algorithm is enhanced by four innovative strategies including dynamical regulation strategy and three search strategies. First, to improve the adaptability of GWO, the dynamical regulation strategy is devised for parameter optimization of GWO. Then, a multi-strategy co-evolution model inspired by HRO is designed, which utilizes neighborhood search, dual-crossover, and selfing techniques to bolster population diversity. Finally, the study develops a hybrid filter-wrapper framework to efficiently select pertinent and informative feature subsets, enhancing the classification performance while conserving time. The performance of HRO-GWO has been rigorously assessed across benchmark functions and the effectiveness of the proposed framework has been evaluated on high-dimensional biomedical datasets. Our experimental findings demonstrate that the approach on the basis of HRO-GWO outperforms state-of-the-art methods. Physical sciences/Mathematics and computing Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Information technology Physical sciences/Mathematics and computing/Scientific data Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Dec, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 17 Jul, 2024 Reviews received at journal 13 Jul, 2024 Reviews received at journal 13 Jul, 2024 Reviews received at journal 07 Jul, 2024 Reviews received at journal 06 Jul, 2024 Reviewers agreed at journal 27 Jun, 2024 Reviews received at journal 27 Jun, 2024 Reviewers agreed at journal 27 Jun, 2024 Reviewers agreed at journal 27 Jun, 2024 Reviewers agreed at journal 26 Jun, 2024 Reviewers agreed at journal 26 Jun, 2024 Reviewers agreed at journal 26 Jun, 2024 Reviewers invited by journal 26 Jun, 2024 Editor assigned by journal 24 Jun, 2024 Editor invited by journal 21 Jun, 2024 Submission checks completed at journal 19 Jun, 2024 First submitted to journal 18 Jun, 2024 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. 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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-4598290","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":319859677,"identity":"a417e383-9da1-4321-9439-c5a299e8ca33","order_by":0,"name":"Zhiwei Ye","email":"","orcid":"","institution":"Hubei University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhiwei","middleName":"","lastName":"Ye","suffix":""},{"id":319859678,"identity":"616d1213-ffc1-4d76-98ad-8326fd2da100","order_by":1,"name":"Ruoxuan Huang","email":"","orcid":"","institution":"Hubei University of 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