An Adaptive Hybrid Differential Grey Wolf Optimization algorithm for WSN coverage.

An Adaptive Hybrid Differential Grey Wolf Optimization algorithm for WSN coverage. | 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 An Adaptive Hybrid Differential Grey Wolf Optimization algorithm for WSN coverage. Yuting Yuan, Yuelin Gao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4630470/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract To address the issues of large blind spots and uneven distribution in traditional Wireless Sensor Network (WSN) node deployment, we propose an Adaptive Hybrid Differential Grey Wolf Optimization (AHDGWO) algorithm for solving the WSN coverage problem in 2D area. Firstly, an adaptive exponential convergence factor is designed, allowing each individual to adjust global exploration and local exploitation adaptively. Secondly, by integrating the concept of differential mutation, an hourglass-shaped random search area is established. This not only prevents blind search but also bolsters the algorithm’s global exploration capabilities. The AHDGWO algorithm’s performance is compared against the standard GWO, its advanced variants and several recent advanced evolutionary algorithms, using the CEC2022 test suite. The results demonstrate that the AHDGWO algorithm achieves better accuracy and convergence speed. Finally, the performance of AHDGWO is evaluated in 2D WSN scenario through simulations. The experimental results show that the coverage rate optimized by the AHDGWO algorithm surpasses that of the other seven comparison algorithms, indicating its practicality and scalability for WSN coverage problems. Wireless Sensor Network Grey Wolf Optimization Coverage issues Differential variation Adaptive exponential convergence factor Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 19 Sep, 2024 Reviews received at journal 11 Sep, 2024 Reviews received at journal 17 Jul, 2024 Reviews received at journal 16 Jul, 2024 Reviewers agreed at journal 12 Jul, 2024 Reviews received at journal 09 Jul, 2024 Reviewers agreed at journal 08 Jul, 2024 Reviewers agreed at journal 08 Jul, 2024 Reviewers agreed at journal 08 Jul, 2024 Reviewers invited by journal 08 Jul, 2024 Editor assigned by journal 27 Jun, 2024 Submission checks completed at journal 26 Jun, 2024 First submitted to journal 24 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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