求解TSP问题的模糊自适应粒子群算法

由于惯性权值的设置对粒子群优化（PSO）算法性能起着关键的作用，本文通过引入模糊技术，给出了一种惯性权值的模糊自适应调整模型及其相应的粒子群优化算法，并用于求解旅行商（TSP）问题。实验结果表明了改进算法在求解组合优化问题中的有效性，同时提高了算法的性能，并具有更快的收敛速度。

Fuzzy Self-Adapted Particle Swarm Optimization Algorithm for Traveling Salesman Problems

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.