Abstract
In this paper, we propose a new Traveling Salesman Problem (TSP) solver based on Particle Swarm Optimization (PSO) Algorithm. PSO is one of the optimization methods classified into swarm intelligence, and consists of particles. Particles interact and move through solution space to find a better solution. PSO can find a good solution in a short time compared with Genetic Algorithm (GA) in many real-valued optimization problems. Because TSP is a combinatorial optimization problem, we change two points of the original PSO for solving TSP. The first point is that the position of each particle is represented by a tour instead of a vector. The second is that particles move by using Insertion method. Insertion method is an operation to combine a tour and sub-paths of other two tour. We analyze relation between parameters and length of an obtained tour, and indicate a guide to adjust parameters. The performance comparison result shows that the proposed method can find a better solution than Simulated Annealing (SA) and GA.
