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The ant colony optimization is a new meta-heuristic, it is a population based algorithm and is a good method for combination optimizations. Due to the random probabilistic search strategy, the slow convergence is the main problem of the ACO. In order to improve the convergence of the algorithm, the premium-penalty ant colony optimization (PPACO) is proposed. In this new algorithm, the good solutions found by the ants are awarded while the ordinary ones are punished. In order to counteract the polarization of pheromone values on all roads, the pheromone trails limited to an interval [taumin, taumax] and the evaporation rate is set to a higher value. The results of the simulation experiments are presented, in which the solutions gained by PPACO is compared with the best known results on several traveling salesman problems. It proves that the PPACO is ranked among the best ACO for tackling the TSP problems.