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<p>The Whale Optimization Algorithm (WOA) is a meta-heuristic algorithm widely utilized in the field of engineering optimization. However, it suffers from defects such as low solution accuracy, slow convergence, and a tendency to fall into local optima. To overcome these shortcomings, this paper proposes a novel approach 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 the elite solution. Second, by introducing a local grey wolf optimal regulation mechanism, a refined search is conducted around the current optimal solution, enhancing solution accuracy and local exploitation capability. Moreover, we improve the random search formula to accelerate convergence. Finally, we introduce a similarity elimination and perturbation mutation strategy, which increases population diversity and enhances the ability to escape local optima. Experimental results on 23 standard test functions and the CEC2019 benchmark demonstrate the significant advantages of EGRMWOA. Compared to five frontier WOA variants, its average ranking reached 1.63 and 1.35, respectively. Furthermore, it maintained excellent average rankings of 1.804 and 2.1 against six other well-known meta-heuristic algorithms, securing the first-place rank overall. In 15 real-world engineering optimization problems, EGRMWOA demonstrates exceptional solving capabilities with an average ranking of 2.833. Crucially, in the practical optimization problem of electric vehicle charging and swapping scheduling, its performance notably surpasses that of the standard WOA and GWO.</p>

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

  • 期刊论文

  • 发表日期：

    2026年7月23日

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  • Guangjun Zai,
  • Junjian Li,
  • Lihong Zhong,
  • Wei She

摘要

The Whale Optimization Algorithm (WOA) is a meta-heuristic algorithm widely utilized in the field of engineering optimization. However, it suffers from defects such as low solution accuracy, slow convergence, and a tendency to fall into local optima. To overcome these shortcomings, this paper proposes a novel approach 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 the elite solution. Second, by introducing a local grey wolf optimal regulation mechanism, a refined search is conducted around the current optimal solution, enhancing solution accuracy and local exploitation capability. Moreover, we improve the random search formula to accelerate convergence. Finally, we introduce a similarity elimination and perturbation mutation strategy, which increases population diversity and enhances the ability to escape local optima. Experimental results on 23 standard test functions and the CEC2019 benchmark demonstrate the significant advantages of EGRMWOA. Compared to five frontier WOA variants, its average ranking reached 1.63 and 1.35, respectively. Furthermore, it maintained excellent average rankings of 1.804 and 2.1 against six other well-known meta-heuristic algorithms, securing the first-place rank overall. In 15 real-world engineering optimization problems, EGRMWOA demonstrates exceptional solving capabilities with an average ranking of 2.833. Crucially, in the practical optimization problem of electric vehicle charging and swapping scheduling, its performance notably surpasses that of the standard WOA and GWO.