An Ant Colony Optimization Algorithm for Solving Traveling Salesman Problem

Abstract

Ant Colony Optimization (ACO) is a class of heuristic search algorithms that have been successfully applied to solving combinational optimization (CO) problems. The traveling salesman problem (TSP) is among the most important combinatorial problems. ACO has very good search capability for optimization problems. But it still has some drawbacks such as stagnation behavior, long computational time, and premature convergence problem of the basic ACO algorithm on TSP. Those problems will be more obvious when the complexities of the considered problems increase. The proposed system based on basic ACO algorithm based on wellpositioned the ants on the initiation and information entropy which is applied to tuning of the algorithm’s parameters. Then, ACO for TSP has been improved by incorporating local optimization heuristic. Therefore, the proposed system intends to reach superior search performance over traditional ACO algorithms do.