An Effective Hybrid Ant Colony Algorithm for Solving the Traveling Salesman Problem

Ant colony optimization (ACO) is a relatively new random heuristic algorithm inspired by the behavior of real ant colony. It has been applied in many combinatorial optimization problems and the traveling salesman problem (TSP) is the basic problem to which it has been applied. In this paper, we propose a hybrid ACO algorithm for the TSP to overcome some shortcomings of the prior ACO. It is an evolutionary ACO based on the minimum spanning tree (MST). The intuition of the proposed algorithm is that the edges in the MST will probably appear in the optimal path of TSP. It takes advantage of the relationship between the MST and the optimal path to limit the search range of the ant in each city. This hybrid algorithm can evolve the optimization strategy and improve the computing speed. Computer simulation results show that the proposed method attains better result and higher efficiency than the previous ant colony algorithms.