Optimizing ACS for Big TSP Problems Distributing Ant Parameters

Ant colony algorithms are a group of heuristic optimization algorithms that have been inspired by ants foraging for food. In these algorithms there are some agents, the ants, that for finding the suitable solution, search the solution space. Ant colony algorithms have some parameters like relative pheromone importance on trail and pheromone decay coefficient that convergence and efficiency of algorithms is highly related to them. Usually desirable value of these parameters regarding the problem is determined through trial and error. Some approaches proposed to adapt parameter of these algorithms for optimizing them. The most important feature of the proposed algorithms are complication and time overhead. In this paper we have presented a simple and efficient approach based on distribution of ant parameters for optimizing ACS algorithm and by using different experiments efficiency of this proposed approach has been evaluated and we have shown that the presented concept is one of the most important reasons in success for parameter adapting algorithms