Comparative Analysis of Genetic Algorithm, Particle Swarm Optimization and Ant Colony Optimization for TSP

As a typical combinatorial problem, the travelling salesman problem (TSP) has attracted extensively research interest. Ant Colony optimization (ACO), Genetic Algorithm (GA), Particle swarm optimization (PSO) stochastic search methods that mimic the natural evolution and/or the social behavior of species, have great potentials to solve the combination optimization problems, respectively used in solving traveling salesman problem. Performance comparative analyses have been done by using ACO, GA and PSO respectively in solving TSP in this paper. The experiments show the advantages and disadvantages used only ACO, GA and PSO. The comparative results are shown. And it is devised that GA is better approach to solve the traveling salesman problem.