Improved ant colony algorithm for Traveling Salesman Problems

Abstract

An improved ant colony algorithm is proposed in this paper for Traveling Salesman Problems (TSPs). In the process of searching, the ants are more sensitive to the optimal path because the inverse of distance among cities is chosen as the heuristic information, while a candidate list is used to limit the number of candidate city. The method of local and global dynamic phenomenon update is used in order to adjust the distribution of phenomenon according to the routes. The method of 2-opt is only used for the current optimal tour, enhancing the convergence speed. The simulation results demonstrate the proposed algorithm works well and efficient.