Convergence analysis of flow direction algorithm and its improvement

Convergence analysis of flow direction algorithm and its improvement | 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 Convergence analysis of flow direction algorithm and its improvement S. P. Li, wenwen ye This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1313832/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Jun, 2023 Read the published version in Soft Computing → Version 1 posted 4 You are reading this latest preprint version Abstract Flow Direction Algorithm (FDA) is a new physics-based optimization algorithm for solving global optimization problems. Although the FDA has shown effectiveness in many areas, there has been a lack of rigorous theoretical guarantees. This paper first proves that FDA is globally convergent with probability 1 by establishing a Markov process model. Furthermore, to enhance the FDA's exploration and exploitation abilities, we propose an improved FDA algorithm (IFDA) by introducing random opposition-based learning and an adaptive neighbour generation strategy. Finally, extensive experiments are investigated on some representative benchmark functions with several state-of-the-art algorithms, demonstrating the proposed algorithm's efficiency and effectiveness. FDA Markov process Random opposition-based learning Adaptive neighbour generation strategy Full Text Supplementary Files highlights.docx Cite Share Download PDF Status: Published Journal Publication published 13 Jun, 2023 Read the published version in Soft Computing → Version 1 posted Reviewers agreed at journal 17 Jan, 2023 Reviewers invited by journal 17 Jan, 2023 Editor assigned by journal 05 Feb, 2022 First submitted to journal 30 Jan, 2022 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-1313832","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":168408699,"identity":"6b318e5f-9624-4e1b-a745-f1e28615b9e7","order_by":0,"name":"S. P. 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