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

Abstract

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.

Keywords

• adaptive step size
• charging station location
• chaotic mapping
• dynamic threshold
• reverse learning
• whale optimization algorithm