Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review

被引：668

作者：

Bonyadi, Mohammad Reza ^{[1
,2
,3
]}

Michalewicz, Zbigniew ^{[1
,3
,4
,5
]}

机构：

[1] Univ Adelaide, Dept Comp Sci, Adelaide, SA 5005, Australia

[2] Univ Queensland, CAI, Brisbane, Qld 4067, Australia

[3] Complexica Pty Ltd, Adelaide, SA 5021, Australia

[4] Polish Acad Sci, Inst Comp Sci, Warsaw, Poland

[5] Polish Japanese Inst Informat Technol, Warsaw, Poland

来源：

EVOLUTIONARY COMPUTATION | 2017年 / 25卷 / 01期

关键词：

Particle swarm optimization; stability analysis; local convergence; invariance; topology; parameter selection; constrained optimization; CMA EVOLUTION STRATEGY; CONSTRAINED OPTIMIZATION; GLOBAL OPTIMIZATION; CONVERGENCE ANALYSIS; ALGORITHM; POPULATION; STABILITY; SELECTION; VELOCITY; SEARCH;

D O I：

10.1162/EVCO_r_00180

中图分类号：

TP18 [人工智能理论];

学科分类号：

140502 [人工智能];

摘要：

This paper reviews recent studies on the Particle SwarmOptimization (PSO) algorithm. The review has been focused on high impact recent articles that have analyzed and/or modified PSO algorithms. This paper also presents some potential areas for future study.

引用

页码：1 / 54

页数：54

共 168 条

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Feature selection for structure-activity correlation using binary particle swarms [J].