Experimental Comparison of GA and PSO methods For Solving Travelling Salesman Problem

For solving hard and complex problems, nature has been main source of inspiration for many years. Nature Inspired heuristic algorithms is getting more popular among the researcher for solving real world NP hard problems like Travelling Salesman Problem(TSP) , Graph Colouring, Vehicle Routing etc. One such classical optimization problem is Travelling Salesman Problem, which is NP hard problem that cannot be solved conventionally particularly when no. of cities increase. It will take years to find optimal solution ,If one tries to solve TSP using conventional approach by considering all possible tours. So, Meta-Heuristic algorithm is the feasible solution to such problem. In this paper we consider three widely used nature inspired heuristic approaches Ant Colony Optimization, Genetic Algorithm and Particle Swarm Optimization to solve TSP. We also compare results of GA and PSO for Instance taken from TSPLIB.