Advance Bat Algorithm Inspired Algorithm for Fast Convergence and High Accuracy in Solving Numerical Optimisation Problems

Advance Bat Algorithm Inspired Algorithm for Fast Convergence and High Accuracy in Solving Numerical Optimisation Problems | 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 Advance Bat Algorithm Inspired Algorithm for Fast Convergence and High Accuracy in Solving Numerical Optimisation Problems M. R. Ramli, Z. A. Abas, F. K. Ahmad, K. M. Zaini This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9045548/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract This paper presents the Advanced Bat Algorithm (ABA), a novel enhancement to the standard Bat Algorithm (BA) designed to address persistent challenges in numerical optimisation, notably slow convergence and susceptibility to local minima in high-dimensional search spaces. ABA introduces three key innovations: physics-based inertia weight modeling, a boundary-aware position update using sine functions, and adaptive boundary control. These modifications collectively improve the balance between exploration and exploitation, ensuring robust performance across a variety of benchmark functions. Extensive computational experiments demonstrate that ABA achieves faster convergence and higher accuracy than standard BA and leading population-based algorithms such as Particle Swarm Optimisation (PSO), Genetic Algorithm (GA), and Harmony Search (HS). Comparative analysis with recent BA variants, including Improved Bat Algorithm (IBA) and Hybrid Self-Adaptive Bat Algorithm (HSABA), further confirms ABA’s superior performance, especially in high-dimensional and complex optimisation scenarios. The findings suggest that ABA is a promising tool for real-world optimisation tasks in 2026, with potential applications in AI model tuning, predictive maintenance, and smart manufacturing. Physical sciences/Engineering Physical sciences/Mathematics and computing Metaheuristic Optimization Swarm Intelligence Predictive Maintenance Convergence Accuracy Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 24 Mar, 2026 Reviews received at journal 23 Mar, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviews received at journal 18 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers invited by journal 18 Mar, 2026 Editor assigned by journal 18 Mar, 2026 Editor invited by journal 18 Mar, 2026 Submission checks completed at journal 17 Mar, 2026 First submitted to journal 16 Mar, 2026 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-9045548","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":608348571,"identity":"30aa0042-13c8-481a-9033-0220418d0e32","order_by":0,"name":"M. R. 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ABA introduces three key innovations: physics-based inertia weight modeling, a boundary-aware position update using sine functions, and adaptive boundary control. These modifications collectively improve the balance between exploration and exploitation, ensuring robust performance across a variety of benchmark functions. Extensive computational experiments demonstrate that ABA achieves faster convergence and higher accuracy than standard BA and leading population-based algorithms such as Particle Swarm Optimisation (PSO), Genetic Algorithm (GA), and Harmony Search (HS). Comparative analysis with recent BA variants, including Improved Bat Algorithm (IBA) and Hybrid Self-Adaptive Bat Algorithm (HSABA), further confirms ABA\u0026rsquo;s superior performance, especially in high-dimensional and complex optimisation scenarios. 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