Enhanced slime mould algorithm with backtracking search algorithm: global optimization and feature selection

Enhanced slime mould algorithm with backtracking search algorithm: global optimization and 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 Enhanced slime mould algorithm with backtracking search algorithm: global optimization and feature selection Jian Wang, Yi Chen, Huilai Zou, Chenglang Lu, Ali Asghar Heidari, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3962990/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 Slime Mould Algorithm (SMA), renowned for its swarm-based approach, encounters challenges, particularly in maintaining a balance between exploration and exploitation, leading to a trade-off that impacts its optimization performance. The simple structure and limited hyperparameters of SMA contribute to difficulties in effectively navigating the exploration-exploitation trade-off, with a drawback being its poor ability for exploration. To address these challenges and enhance SMA, this paper introduces BSSMA, an improved variant that incorporates the Backtracking Search Algorithm (BSA). The introduction of the \(phaseratio\) parameter aims to synergize BSA and SMA, capitalizing on the strengths of both algorithms while mitigating their individual drawbacks, including SMA's poor exploration ability. BSA facilitates a thorough exploration, dispersing search agents widely across the solution space, ensuring significant diversity. These search agents then transition to SMA to further refine the search for optimal solutions while addressing SMA's exploration limitations. Evaluating the performance of BSSMA involves comparisons with 12 other meta-heuristic algorithms (MAs) and 10 advanced MAs using the CEC2017 benchmark functions. Experimental results showcase that the enhanced BSSMA outperforms SMA in terms of convergence speed and accuracy, specifically addressing the challenges associated with balancing exploration and exploitation trade-offs, including SMA's poor exploration ability. Additionally, to demonstrate BSSMA's effectiveness in practical engineering applications, a binary version (bBSSMA) is developed for feature selection (FS) using a V-shaped transfer function. Comparative experiments with seven other binary MA variants reveal that bBSSMA selects fewer features, attains higher classification accuracy, and demands less computational time. These results affirm the effectiveness of bBSSMA for practical feature selection applications. Slime mould algorithm Backtracking search algorithm Swarm intelligence Feature selection 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-3962990","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273642727,"identity":"176fa5e5-b037-43b6-bdbc-f880ce72b645","order_by":0,"name":"Jian Wang","email":"","orcid":"","institution":"Wenzhou University","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Wang","suffix":""},{"id":273642728,"identity":"27baf6c7-9b8e-42ba-9e0c-640dd61e2a52","order_by":1,"name":"Yi Chen","email":"","orcid":"","institution":"Wenzhou 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The simple structure and limited hyperparameters of SMA contribute to difficulties in effectively navigating the exploration-exploitation trade-off, with a drawback being its poor ability for exploration. To address these challenges and enhance SMA, this paper introduces BSSMA, an improved variant that incorporates the Backtracking Search Algorithm (BSA). The introduction of the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(phaseratio\\)\u003c/span\u003e\u003c/span\u003e parameter aims to synergize BSA and SMA, capitalizing on the strengths of both algorithms while mitigating their individual drawbacks, including SMA's poor exploration ability. BSA facilitates a thorough exploration, dispersing search agents widely across the solution space, ensuring significant diversity. These search agents then transition to SMA to further refine the search for optimal solutions while addressing SMA's exploration limitations. 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