Improved Whale Optimization Algorithm Based on Hybrid Strategy and Its Application in Location Selection for Electric Vehicle Charging Stations

The charging station location model is a nonlinear programming model with complex constraints. In order to solve the problems of weak search ability and low solution accuracy of the whale optimization algorithm (WOA) in solving location models or high-dimensional problems, this paper proposes an improved whale optimization algorithm (IWOA) based on hybrid strategies. Chaos mapping and reverse learning mechanism are introduced in the original algorithm, and the change mode of convergence factor and probability threshold is improved. Through optimization experiments on 18 benchmark functions, the test results show that IWOA has the best solution ability. Finally, IWOA is used to solve a site selection optimization model aiming at the minimum comprehensive cost. The results show that the proposed algorithm and model can effectively reduce the comprehensive cost of site selection. This provides a necessary decision-making reference for the scientific site selection for electric vehicle charging stations.

Automated summary

This paper proposes an improved whale optimization algorithm (IWOA) based on hybrid strategies to enhance the search ability and solution accuracy of the original algorithm in solving location models or high-dimensional problems. The use of chaos mapping and reverse learning mechanism, along with improved convergence factor and probability threshold, greatly improves the solution capability of IWOA. The experimental results demonstrate the effectiveness of IWOA in solving optimization problems and reducing the comprehensive cost in site selection for electric vehicle charging stations.