Dynamic Swordfish Movement Optimization Algorithm for Feature Selection

Dynamic Swordfish Movement Optimization Algorithm for 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 Research Article Dynamic Swordfish Movement Optimization Algorithm for Feature Selection Faris H. Rizk, Khaled Sh.Gaber, Marwa M. Eid, Doaa Sami Khafaga, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7087991/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Dec, 2025 Read the published version in Journal of Big Data → Version 1 posted 10 You are reading this latest preprint version Abstract Feature selection represents a crucial preprocessing step in machine learning pipelines, particularly when dealing with high-dimensional datasets that often contain redundant or irrelevant information. To address the challenge of efficiently selecting informative features while optimizing classification accuracy, this paper introduces the Dynamic Binary Swordfish Movement Optimization Algorithm (DBSMOA), a novel binary metaheuristic inspired by the foraging 1 behavior of swordfish. DBSMOA enhances the original Swordfish Movement Optimization Algorithm (SMOA) by incorporating dynamic behavioral mechanisms that adaptively balance exploration and exploitation throughout the search process. The proposed algorithm employs a binary encoding strategy based on sigmoid probabilistic mapping and dynamically alternates agent roles using real-time performance metrics and elitist selection strategies. To assess its efficacy, DBSMOA is extensively evaluated on diverse benchmark datasets and compared against twelve state-of-the-art binary optimizers, including Binary Particle Swarm Optimization (bPSO), Binary Genetic Algorithm (bGA), and Binary Grey Wolf Optimizer (bGWO). The results demonstrate that DBSMOA consistently achieves superior performance in classification accuracy, feature reduction, and computational efficiency. These findings highlight the robustness and adaptability of DBSMOA for binary optimization problems, as well as its practical potential for high-dimensional data analytics. Dynamic Binary Optimization Feature Selection Metaheuristic Algorithms Swordfish Movement Optimization High-Dimensional Data Analysis Full Text Additional Declarations No competing interests reported. Supplementary Files SpringerDynamicBinarySwordfish.zip Cite Share Download PDF Status: Published Journal Publication published 27 Dec, 2025 Read the published version in Journal of Big Data → Version 1 posted Editorial decision: Revision requested 30 Sep, 2025 Reviews received at journal 18 Sep, 2025 Reviewers agreed at journal 27 Aug, 2025 Reviews received at journal 27 Aug, 2025 Reviewers agreed at journal 27 Aug, 2025 Reviewers agreed at journal 26 Aug, 2025 Reviewers invited by journal 25 Aug, 2025 Editor assigned by journal 24 Aug, 2025 Submission checks completed at journal 10 Jul, 2025 First submitted to journal 09 Jul, 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-7087991","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":483359061,"identity":"2334f7a7-ad0c-4da5-8093-0bb534ee50fd","order_by":0,"name":"Faris H. 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To address the challenge of efficiently selecting informative features while optimizing classification accuracy, this paper introduces the Dynamic Binary Swordfish Movement Optimization Algorithm (DBSMOA), a novel binary metaheuristic inspired by the foraging 1 behavior of swordfish. DBSMOA enhances the original Swordfish Movement Optimization Algorithm (SMOA) by incorporating dynamic behavioral mechanisms that adaptively balance exploration and exploitation throughout the search process. The proposed algorithm employs a binary encoding strategy based on sigmoid probabilistic mapping and dynamically alternates agent roles using real-time performance metrics and elitist selection strategies. To assess its efficacy, DBSMOA is extensively evaluated on diverse benchmark datasets and compared against twelve state-of-the-art binary optimizers, including Binary Particle Swarm Optimization (bPSO), Binary Genetic Algorithm (bGA), and Binary Grey Wolf Optimizer (bGWO). The results demonstrate that DBSMOA consistently achieves superior performance in classification accuracy, feature reduction, and computational efficiency. 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