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

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.