Ant colony optimization (ACO) has been proved to be one of the best performing algorithms for NP-hard problems as TSP. Many strategies for ACO have been studied, but fewer tuning methodologies have been done on ACO's parameters which influence the algorithm directly. The setting of ACO's parameters is considered as a combinational optimization problem in this paper. The particle swarm optimization (PSO) is introduced to solve this problem, and an adaptive parameter setting strategy is proposed. It's proved to be effective by the experiment based on TSPLIB test