In this paper, we propose the cAS, a new ACO algorithm, and evaluate the performance using TSP instances available at TSPLIB. The results show that cAS works well on the test instances and has performance that may be one of the most promising ACO algorithms. We also evaluate cAS when it is combined with LK local search heuristic using larger sized TSP instances. The results also show promising performance. cAS introduced two important schemes. One is to use the colony model divided into units, which has a stronger exploitation feature while maintaining a certain degree of diversity among units. The other is to use a scheme, we call cunning, when constructing new solutions, which can prevent premature stagnation by reducing strong positive feedback to the trail density.