粒子群优化算法中惯性权重改进策略综述  

Summary on improved inertia weight strategies for particle swarm optimization algorithm

作　　者：杨博雯 钱伟懿[1] YANG Bowen;QIAN Weiyi(College of Mathematics and Physics,Bohai University,Jinzhou 121013,China)

机构地区：[1]渤海大学数理学院

出　　处：《渤海大学学报（自然科学版）》2019年第3期274-288,共15页Journal of Bohai University:Natural Science Edition

基　　金：国家自然科学基金项目(No:11371071)

摘　　要：在粒子群优化算法的3个参数中,惯性权重是最重要的参数,它对粒子群优化算法性能的提高起到至关重要作用.因此许多学者对粒子群优化算法中的惯性权重设计进行了广泛研究,目前取得许多成果.本文介绍了基本粒子群优化和标准粒子群优化算法,综述了惯性权重在粒子群优化算法中的各种改进策略.为粒子群优化算法的进一步改进研究提供参考.Among the three parameters of particle swarm optimization algorithm,inertia weight is the most important parameter,which plays an important role in improving the performance of particle swarm optimization algorithm.Therefore,many scholars have studied the design of inertia weight in particle swarm optimization extensively,and many achievements have been made.This paper introduces the basic particle swarm optimization and standard particle swarm optimization algorithm,and summarizes the various improvement strategies of inertia weight in particle swarm optimization algorithm.It provides a reference for further improvement of particle swarm optimization algorithm.

关 键 词：粒子群优化算法 惯性权重 全局搜索能力 局部搜索能力 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]