An Ant Colony Algorithm Assisted by Graph Neural Networks for Solving Vehicle Routing Problems

Abstract

Vehicle routing problems have attracted increasing attention because of the rapid development of transportation. Companies want to reduce the cost by lowering the number of vehicles and the total distances, which can be considered as a combinatorial optimization problem. The ant colony algorithm shows great potential in solving vehicle routing problems. However, it suffers from a low convergence speed due to the randomly initialized pheromone, which may cause a waste of computational resources in the early search process. To address this problem, a graph neural network is pre-trained to provide prior knowledge to initialize the pheromone in the ant colony algorithm, which can boost the convergence process. In addition, some classic local research methods are applied to balance the exploration and exploitation of the evolutionary process.