Date of Publication:2022-10-04

Journal:吉林大学学报 工学版

Affiliation of Author(s):电子信息与电气工程学部

Issue:2

Page Number:615-623

ISSN No.:1671-5497

Abstract: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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