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

Yongjing Li, Wenhui Pei and Qi Zhang
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Yongjing Li: School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan 250357, China
Wenhui Pei: School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan 250357, China
Qi Zhang: School of Control Science and Engineering, Shandong University, Jinan 250061, China

Energies, 2022, vol. 15, issue 19, 1-25

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 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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