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

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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
    State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China)

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.

Suggested Citation

  • Yongjing Li & Wenhui Pei & Qi Zhang, 2022. "Improved Whale Optimization Algorithm Based on Hybrid Strategy and Its Application in Location Selection for Electric Vehicle Charging Stations," Energies, MDPI, vol. 15(19), pages 1-25, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7035-:d:924550
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    References listed on IDEAS

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    1. Guiliang Gong & Raymond Chiong & Qianwang Deng & Xuran Gong, 2020. "A hybrid artificial bee colony algorithm for flexible job shop scheduling with worker flexibility," International Journal of Production Research, Taylor & Francis Journals, vol. 58(14), pages 4406-4420, July.
    2. Di Xu & Wenhui Pei & Qi Zhang, 2022. "Optimal Planning of Electric Vehicle Charging Stations Considering User Satisfaction and Charging Convenience," Energies, MDPI, vol. 15(14), pages 1-16, July.
    3. Gui-Ying Ning & Dun-Qian Cao & Manuel De la Sen, 2021. "Improved Whale Optimization Algorithm for Solving Constrained Optimization Problems," Discrete Dynamics in Nature and Society, Hindawi, vol. 2021, pages 1-13, February.
    4. Shuxia Yang & Di Zhang & Jing Fu & Shujing Fan & Yu Ji, 2018. "Market Cultivation of Electric Vehicles in China: A Survey Based on Consumer Behavior," Sustainability, MDPI, vol. 10(11), pages 1-23, November.
    5. Wu, Jingwen & Liao, Hua & Wang, Jin-Wei, 2020. "Analysis of consumer attitudes towards autonomous, connected, and electric vehicles: A survey in China," Research in Transportation Economics, Elsevier, vol. 80(C).
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    Cited by:

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