The traveling salesman problem (TSP) is one of the most important problems that has received much attention because of its practical applications in industrial problems.In this problem a salesman starts to move from a certain node which is called node 0 and returns after visiting n nodes so that each node is visited only once. The purpose is to find the shortest path in order to keep the cost at minimum. The existence of several TSP inspired problems like vehicle routing problem, the capability of changing other problems into this problem and solving them with the existing algorithms like sequencing job problem, and using this problem as a benchmark for testing new algorithms are the reasons which have attracted a lot of researchers’ attention and encouraged them to analyze it in recent years. In this paper, a modified ant colony optimization (MACO) is presented for solving the TSP in which some local search algorithms are utilized as an effective criterion for escaping from the local optimum points. In the proposed algorithm, only a global updating is used in order to increase pheromone on the edges of the best route and will at the same time decrease the amount of pheromone on the edges of the worst route. The proposed algorithm is tested on several TSP instances involving from 48 to 200 nodes from the literature. The computational result shows that the proposed algorithm is able to improve the efficiency of the classical ACO in all instances and is competitive with and other meta-heuristic algorithms results for solving TSP.
