A Novel algorithm of Genetic Ant Colony Optimization (GACO) for Traveling Salesman Problem

In this paper, a novel algorithm of genetic ant colony optimization (GACO) is proposed for traveling salesman problem (TSP). TSP is to minimize the cost of travel of a salesman in visiting all the cities in a given set, and return to the starting city. Basically, the proposed algorithm combines ant colony optimization (ACO) with genetic algorithm (GA) and can explore and exploit search spaces. It has both the advantage of ACO, the ability to find feasible solutions and to avoid premature convergence, and that of GA, the ability to avoid being trapped in local optima. In this paper, variant test problems are drawn from randomly generated data and the most well known TSP problems chosen from TSPLIB. Simulation results are reported, and the proposed algorithm seems to have admirable performance.