An elited-weights whale optimization algorithm with local grey wolf optimal regulation for solving charging and swapping scheduling problem

An elited-weights whale optimization algorithm with local grey wolf optimal regulation for solving charging and swapping scheduling problem | 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 elited-weights whale optimization algorithm with local grey wolf optimal regulation for solving charging and swapping scheduling problem Guangjun Zai, Junjian Li, Lihong Zhong, Wei She This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6760084/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Whale Optimization Algorithm (WOA) is a meta-heuristic algorithm widely utilized in the field of engineering optimization. However, it has defects such as the low solution accuracy, slower convergence, and a tendency to fall into local optimum.To overcome these shortcomings, a novel approach is proposed in this paper, named EGRMWOA.First, we design an adaptive elite weight mechanism to achieve a balance between global exploration and local exploitation by dynamically adjusting the influence of elite solution.Second, by introducing a local grey wolf optimal regulation, a refined search is conducted around the current optimal solution, enhancing the solution accuracy and local development capability.Moreover, we improve the random search formula to help convergence.Finally, we introduce a similarity elimination and perturbation mutation strategy, this increases population diversity and enhances the ability to escape local optima.The results on 23 international test functions and CEC2019 show that EGRMWOA outperforms many WOA variants and other well-known meta-heuristic algorithms.In 15 real-world engineering optimization problems, EGRMWOA demonstrates exceptional solving capabilities with an average ranking of 2.833.In the practical optimization problem of electric vehicle charging and swapping scheduling, its performance surpasses that of WOA and GWO. Whale optimization algorithm Swarm-intelligence Adaptive elite weights Local grey wolf optimal regulation Charging and swapping scheduling Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 16 Sep, 2025 Reviews received at journal 02 Jul, 2025 Reviews received at journal 27 Jun, 2025 Reviewers agreed at journal 23 Jun, 2025 Reviews received at journal 17 Jun, 2025 Reviewers agreed at journal 17 Jun, 2025 Reviewers agreed at journal 17 Jun, 2025 Reviewers invited by journal 17 Jun, 2025 Editor assigned by journal 28 May, 2025 Submission checks completed at journal 27 May, 2025 First submitted to journal 27 May, 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. 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