Improved ant agents system by the dynamic parameter decision

The ant colony system (ACS) algorithm is a new meta-heuristic for hard combinational optimization problems. It is a population-based approach that uses the exploitation of positive feedback as well as a greedy search. It was first proposed for tackling the well-known traveling salesman problem (TSP). In this paper, we introduce a new version of the ACS based on a dynamic weighted updating method and a dynamic ant number decision method using a curve-fitting algorithm. An implementation to solve the TSP and performance results under various conditions are presented, and a comparison between the original ACS and the proposed method is shown. It turns out that our proposed method can compete with the original ACS in terms of solution quality and computation speed for these problems.