A Multifidelity-Based Ant Colony Optimization Algorithm for Capacitated Electric Vehicle Routing Problems

Chengming Wu, Xiaoshu Xiang, Hao Jiang, Xingyi Zhang

Abstract: The capacitated electric vehicle routing problem (CEVRP) has drawn much attention from researchers in the recent decade against the background of the rising electric transportation industry. Existing studies have found the CEVRP more difficult to address than the typical CVRP since the CEVRP needs to simultaneously optimize routing plans and charging decisions at a high computational budget. Given a certain routing plan, it takes much computational cost to exhaustively or approximately achieve the accurate optimal charging decision under the routing plan. This paper proposes that it is unnecessary to search for the accurate optimal charging decisions for potentially low-quality routing plans found during the CEVRP optimization, and instead obtaining acceptable charging decisions significantly reduces the computational cost and helps maintain fast convergence. A multifidelity-based ant colony optimization (MFACO) algorithm is then proposed to flexibly search charging decisions based on the potential quality of routing plans so that CEVRPs can be addressed at high efficiency. The proposed MFACO employs a low-fidelity search strategy to obtain coarse charging decisions for potentially low-quality routing plans, whereas for potentially high-quality routing plans MFACO employs three high-fidelity search strategies to local search in different regions of decision space and provide accurate optimal charging decisions. Experimental results demonstrate that the proposed multifidelity method enhances the efficiency of ACO in solving CEVRPs and the proposed MFACO significantly outperforms four state-of-the-art algorithms for CEVRPs, providing a competitive performance in terms of both solution quality and computational cost.

External IDs:doi:10.1109/tits.2025.3624831