Pheromone trail initialization with local optimal solutions in ant colony optimization

This paper presents a method to improve the search rate of Max-Min Ant System for the traveling salesman problem. The proposed method gives deviations from the initial pheromone trails by using a set of local optimal solutions calculated in advance. Max-Min Ant System has demonstrated impressive performance, but the rate of search is relatively low. Considering the generic purpose of stochastic search algorithms, which is to find near optimal solutions subject to time constraints, the rate of search is important as well as the quality of the solution. The experimental results using benchmark problems with 51 to 318 cities suggested that the proposed method is better than the conventional method in both the quality of the solution and the rate of search.