微粒群算法中惯性权重的调整策略

惯性权重是微粒群算法中的关键参数，可以平衡算法全局搜索能力和局部搜索能力的关系，提高算法的收敛性能。该文分析了惯性权重对微粒群算法收敛性能的影响，为了进一步提高算法的全局最优性，提出了几种对惯性权重的调整策略。通过对4个测试函数的仿真实验，验证了这些策略的可行性，表明这些策略能够简便高效地提高算法的全局收敛性和收敛速度。

Selection on Inertia Weight of Particle Swarm Optimization

The inertia weight is the crucial parameter of the particle swarm optimization(PSO). It can balance the global search and local search to improve PSO’s convergence. This paper analyzes the effect of inertia weight on PSO’s performance. To enhance the global optimality, a few adjusting methods on inertia weight are put forward. The results on four benchmark functions prove these methods are feasible, and indicate these methods can improve the global convergence and convergence speed.