An Ant System-Assisted Genetic Algorithm For Solving The Traveling Salesman Problem

Abstract

The travelling salesman problem (TSP) is a classic problem of combinatorial optimization and has applications in planning, scheduling, and searching in many scientific and engineering fields. Genetic algorithms (GA) and ant colony optimization (ACO) have been successfully used in solving TSPs and many associated applications in the last two decades. However, both GA and ACO have difficulty in regularly reaching the global optimal solutions for TSPs. In this paper, we propose a new hybrid algorithm, ant system-assisted genetic algorithm (ASaGA) to handle this problem. The main change in ASaGA is to use the results of ACO to replace that of GA after every certain number of runs during the process. This provides a new mechanism to steer GA out of potential stagnation at local optima and thus enhances the chance in reaching the global optimal solution. Our simulation on three benchmark TSPs shows that this AS-assisted GA (ASaGA) algorithm can significantly improve quality of optimal solutions with a small increase in computing cost.