In the fields of ant colony optimization (ACO), models of collective intelligence of ants are transformed into useful optimization techniques. A kind of improved ACO (named PMACO) approach for traveling salesman problems (TSP) is presented. Aimed at the disadvantages existed in ACO, several new betterments are proposed and evaluated. In particular, the option that an ant hunts for the next step, the use of a combination of two kinds of pheromone evaluation models, the change of amount in the ant colony during the run of the algorithm, and the mutation of pheromone are studied. We tested ACO algorithm on a set of benchmark problems from the Traveling Salesman Problem Library. It performed better than the original and the other improved ACO algorithms.