An Ant Colony Optimization Algorithm Based on the Experience Model

In this paper, we propose a new model of Ant Colony Optimization (ACO) to solve the traveling salesman problem (TSP). In the new model, we introduce the experience value and a new structure called map, and the experience value is embodied in the reliability of the maps. Each ant takes a map; all ants are guided by the experience gaining from the maps so that the system based on the new model is able to converge into a near-optimum solution quickly. We have proposed an algorithm of MMAS based on the new model, tested the algorithm on several TSP instances and discussed parameter selection. We experimentally show that the algorithm based on the new model improves the converging speed and can find better solutions in comparison with the algorithm which does not use the new model.