An improved ant colony algorithm with soldier ants

In the traveling Salesman Problem (TSP) research, the global search capability, convergence speed and robustness have become the hot issues. The ant colony algorithm is often used to solve TSP. The paper presents an improved algorithm based on the basic ant colony algorithm. In order to avoid convergence premature, the algorithm introduces the concept of soldier ants. So the algorithm is called as Soldier Ants Ant Colony Algorithm. It is abbreviated as SAACA in the paper. There are two species ants that are soldier ants and ordinary ants in SAACA. They are inspired by different factors in the search. The distribution of soldier ants will affect the movement of ordinary ants, and the attraction for ordinary ants decreased on the location where soldier ants have. Thus the algorithm has stronger global search capability. In the paper the SAACA is used to solve TSP. The ratio of two kinds of ants on the initial moment is determined by the experiment. Experimental results show that the number of iterations of the algorithm reduces several times or even ten times more than the basic ant colony algorithm. Besides this algorithm has stronger robustness.