Fuzzy Self-Adapted Particle Swarm Optimization Algorithm for Traveling Salesman Problems
求解TSP问题的模糊自适应粒子群算法

The Particle swarm optimization(PSO)is an algorithm for finding optimal regions of complex search spaces through the interaction of individuals in a population of particles. The setting of inertia weight plays a key role in the performance of PSO, so many presented improved PSO algorithms based inertia weight were advanced. Based on fuzzy technology, a new fuzzy self-adapted model of inertia weight and corresponding PSO are proposed in the paper, then this paper proposes its application to traveling salesman problems(TSP). In the new PSO, different inertia weights are used in updating the particle swarm in a same generation. The experiments show that the new PSO algorithm can achieve good results. Compared with the linearly decreasing inertia weight PSO, the new algorithm also improves the performance of PSO and speeds up the velocity of the PSO convergence.