一种动态改变惯性权重的粒子群优化算法  

A Modified Particle Swarm Optimizer Using Dynamic Inertia Weight

作　　者：王启付[1] 王战江[1] 王书亭[1] 

机构地区：[1]华中科技大学,武汉430074

出　　处：《中国机械工程》2005年第11期945-948,共4页China Mechanical Engineering

基　　金：国家重点基础研究发展计划资助项目(2003CB716207)

摘　　要：针对粒子群优化算法的局限性,提出了一种动态改变惯性权重的粒子群算法,在优化迭代过程中,惯性权重值随粒子的位置和目标函数的性质而变化。函数测试表明,改进后的算法使收敛速度显著加快,而且不易陷入局部极值点。Because of the limitation of particle swarm optimization, a modified particle swarm optimizer which adopted the dynamic inertia weight was proposed. The dynamic inertia weight was changed in every loop according to the particles' positions and the objective function. From experimental results it can be concluded that using a dynamic inertia weight makes the rapidity of convergence accelerate and is not easy to trap in the local extreme points.

关 键 词：粒子群 优化算法 动态惯性权重 收敛速度 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]