Ant Colony Optimization With Deep Heuristic Information for Solving Electric Vehicle Routing Problems

The electric vehicle routing problem (EVRP) is a complex logistical challenge that requires optimizing both route planning and charging decisions. Although ant colony optimization (ACO) has demonstrated potential in solving the EVRP, conventional approaches often rely on simplistic distance-based heuristics that inadequately capture the problem’s global characteristics, resulting in low search efficiency and limited generalization capability. To overcome these limitations, we propose a novel ACO algorithm enhanced with deep heuristic information (ACO-DHI), where a learning model is designed to extract richer heuristic information from a large number of offline instances. This model incorporates a heterogeneous attention mechanism to capture the intrinsic relationships between customers and charging stations, and a feature refinement method to gain more relevant problem-specific information. Once offline training is completed, the model outputs a deep heuristic information matrix, which replaces the traditional distance matrix in ACO for online optimization. Additionally, a new pheromone updating strategy is designed specifically for EVRP, adaptively updating pheromone levels in response to different visits to customers and charging stations. Extensive computational experiments on public benchmark instances demonstrate that ACO-DHI exhibits faster solution speed and stronger generalization capability compared to existing learning methods and heuristics. Code is avaiable at https://github.com/ACO-DHI/ACO-DHI.