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发表时间：2022-10-04

发表刊物：吉林大学学报 工学版

所属单位：电子信息与电气工程学部

期号：2

页面范围：615-623

ISSN号：1671-5497

摘要：Particle Swarm Optimization (PSO) is a swarm intelligence based optimization algorithm.It is simple in concept,easy in implementation and fast in searching.This paper aims at improving the weak premature convergence shortcoming of traditional PSOs.Two sufficient conditions,i.e.global condition and local condition,which guarantee PSO converging to the optimality region,are proposed and verified.Moreover,two PSO variants that have Cauchy stochastic character and Gaussian stochastic character are designed based on the proposed conditions.The experimental results show that the proposed global convergent PSOs can solve the optimization problems effectively.

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