An Exponential Entropy-based Hybrid Ant Colony Algorithm for Vehicle Routing Optimization

Vehicle routing problem(VRP) is important combinatorial optimization problems which have received considerable attention in the last decades. The optimization of vehicle routing problem is a well-known research problem in the logistics distribution. In order to overcome the prematurity of Ant Colony Algorithm (ACA) for logistics distribution routing optimization, a hybrid algorithm combining improved ACA with Iterated Local Search (ILS) is proposed. The proposed algorithm adjusts the pheromone trail to balance the convergence rate and diversification of solutions self-adaptively. The exponential entropy is used to control the path selection and pheromone updating strategy. Combining with ILS is to avoid local best solutions and accelerate the search. Computational results denote the efficiency of the proposed algorithm on some standard benchmark problems.