基于改进粒子群优化算法求解旅行商问题

Solving Traveling Salesman Problem Based on Improved Particle Swarm Optimization Algorithm

本文提出了一种改进粒子群优化算法:在算法中引入了速度变异机制和粒子自探索机制。这种改进后的学习行为更符合自然界生物的学习规律,更有利于粒子发现问题的全局最优解。用改进后的粒子群算法求解标准的旅行商问题,数字仿真表明了算法有效性。

An improved particle swarm optimization algorithm is proposed, the mutation of velocity and the tentative behavior of particles have been introduced according to the phenomena of nature. In this way, the behavior of particles accords with the biological natural law even more, and easily find the global optimum solution. For solving benchmark traveling salesman problem, numerical simulation results shows the effectiveness and efficiency of the proposed method.