改进的粒子群算法在旅行商问题中的应用

针对基本粒子群优化算法(PSO)容易陷入局部最优的缺点，将模拟退火算法(SA)引入PSO，提出一种新的粒子群算法求解旅行商问题。该算法结合了PSO的快速寻优能力和SA的概率突跳特性，保证了群体的多样性，避免了种群的退化。通过与SA、基本遗传算法和基本蚁群算法进行对比实验，证明了该算法求解TSP的效果最好，且简单易实现、实用性较高。

Application of Improved Particle Swarm Optimization in TSP

To overcome the shortcoming of basic Particle Swarm Optimization(PSO) that it is easy to trap into local minimum, this paper proposes an advanced PSO with Simulating Annealing(SA) and applies the new algorithm in solving Traveling Salesman Problem(TSP). SA is used to slow down the degeneration of the PSO swarm and increase the swarm’s diversity. Experiments are made to compare the algorithm with PSO-SA, basic GA, basic SA and basic ACA on solving TSP problem. Results show that PSO-SA is superior to other methods.