粒子群优化算法(PSO)是一类随机全局优化技术，算法简单、容易实现而功能强大，目前己成为国际进化计算界研究的热点.粒子群算法的性能受到参数惯量权重。的影响，大量研究表明，较小的
。具有较好的局部搜索能力，可提高求解精度;较大的。具有较好的全局搜索能力，在一定程度上可以避免陷入局部最优.很多研究者提出了多种动态调整惯量权重的方法.木文系统地介绍和分析比较了目
前动态调整惯量权重的通种典型方法，即线性递减惯量权重、随机惯量权重、凹函数递减惯量权重和凸函数递减惯量权重.为了调查这些控制方法对PSO性能的影响，木文在10个不同的单峰和多峰函数上
系统地对这通种方法进行了测试和比较，完整的实验结果比较分析对选择合适的参数控制方法以求解单峰函数和多峰函数具有一定的指导作用.
Abstract
Particle swarm optimization (PSO) is a kind of global optimization technique which is simple，easy for implementation，and powerful. As the inertia weight parameter affects the algorithm performance significantly, this
paper makes a systematic introduction and comparisons on the current typical inertial weight control approaches including linearly decreasing approach, random approach
approach.The research is based on 10 different unimodal concave decreasing approach，and convex decreasing and multimodal benchmark functions.The experimental
results and the comparisons can give guidelines to researchers so as to choice appropriate inertia w eight control approach to solve different problem efficiently.
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