粒子群优化算法惯量权重控制方法的研究*

南京大学学报(自然科学版) ›› 2011, Vol. 47 ›› Issue (4): 364–371.

粒子群优化算法惯量权重控制方法的研究*

刘杨¹，田学锋²，詹志辉^1**

出版日期:2015-04-17 发布日期:2015-04-17
作者简介: (1.中山大学计算机科学系，广州510275;2.中兴通讯股份有限公司，深圳，518057)
基金资助:

Research on inertia weight control approaches in particle swarm optimization

LiuYang¹ Tian Xue Fenh²，Zhan Zhi一Hui¹

Online:2015-04-17 Published:2015-04-17
About author: (1 .Department of Computer Science Sun Yat-sen University, Guangzhou，510275 , China
2. Zhongxing Telecom Equipment Corporation, Shcnzhen, Uuangdong, 518057,China)

摘要/Abstract

摘要： 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.

刘杨¹，田学锋²，詹志辉^1**
. 粒子群优化算法惯量权重控制方法的研究*
[J]. 南京大学学报(自然科学版), 2011, 47(4): 364–371.

LiuYang¹ Tian Xue Fenh²，Zhan Zhi一Hui¹
. Research on inertia weight control approaches in particle swarm optimization
[J]. Journal of Nanjing University(Natural Sciences), 2011, 47(4): 364–371.

参考文献

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