Application of Ant Colony Algorithms to Solve the Vehicle Routing Problem

Abstract

Many optimization problems exist in the world. The Vehicle Routing Problem (VRP) is a relatively complex and high-level issue. The ant colony algorithm has certain advantages for solving the capacity-based vehicle routing problem (CVRP), but is prone to local optimization and high search speed problems. To solve these problems, this paper proposes an adaptive hybrid ant colony optimization algorithm to solve the vehicle routing problem with larger capacity. The adaptive hybrid ant colony optimization algorithm uses a genetic algorithm to adjust the pheromone matrix algorithm, designs an adaptive pheromone evaporation rate adjustment strategy, and uses a local search strategy to reduce computation. Experiments on some classic problems show that the proposed algorithm is effective for solving vehicle routing problems and has good performance. In the experiment, the results of different scale issues were compared with previously published papers. Experimental results show that the algorithm is feasible and effective.