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Dual ant colony optimization for electric vehicle charging infrastructure planning

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Abstract

Charging infrastructure planning (CIPL) is key to popularizing electric vehicles and reducing carbon emissions. CIPL consists of two subproblems: charging station siting and charging pile allocation. The existing methods independently solve the two subproblems and ignore their interaction, which restricts the rationality of CIPL. To address this issue, this paper proposes a dual ant colony optimization for CIPL (DACO-CIPL). In each iteration, under the guidance of heuristic information and pheromones, the upper and lower ant colonies construct solutions for charging station siting and charging pile allocation in turn, respectively. Then, a global pheromone update strategy is performed to update the pheromones of each ant colony according to the historical best solutions, which realizes information transmission from the lower ant colony to the upper ant colony. In addition, whenever the upper ant colony finishes constructing solutions, a pheromone enhancement strategy is used to strengthen the pheromones of the lower ant colony according to the solutions of the upper ant colony, which realizes information transmission from the upper ant colony to the lower ant colony. DACO-CIPL is compared with several algorithms on multiple test instances. The experimental results show that DACO-CIPL has superior performance and more reasonable options for CIPL.

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Acknowledgements

The authors sincerely thank peer experts for their guidance and advice. This work is partly supported by NSFC Research Program (61906010, 62276010) and R&D Program of Beijing Municipal Education Commission (KM202010005032, KM202210005009).

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  1. Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, College of Computer Science, Beijing University of Technology, Beijing, China

    Junzhong Ji, Yuefeng Liu & Cuicui Yang

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  1. Junzhong Ji
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  2. Yuefeng Liu
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Correspondence to Cuicui Yang.

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Ji, J., Liu, Y. & Yang, C. Dual ant colony optimization for electric vehicle charging infrastructure planning. Appl Intell 53, 26690–26707 (2023). https://doi.org/10.1007/s10489-023-04772-5

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