Hybrid Fuzzy C-means Clustering and Particle Swarm Optimization Algorithms for Traveling Salesman Problems

Abstract

It is true that the normal TSP can be proved as the well-known NP-Complete (Non-Deterministic Polynomial) path searching problems. The more cities’ nodes number will cause more complex traveling path problems. This paper combines the Particle Swarm Optimization (PSO), Transfer Space (TS) and Simulated Annealing (SA) to build the PSO-TS-SA algorithm. The Fuzzy C-means Clustering (FCM) algorithm is determined to reduce the complexity of large scale traveling cities. From the experiments on several TSP, the proposed hybrid fuzzy C-means clustering and particle swarm optimization Algorithms achieve more accuracy in the lower cost of computation time.