Research on particle swarm optimization: a review

Particle swarm optimization (PSO) explores global optimal solution through exploiting the particle's memory and the swarm's memory. Its properties of low constraint on the continuity of objective function and joint of search space, and ability of adapting to dynamic environment make PSO be...

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Bibliographic Details
Published inProceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826) Vol. 4; pp. 2236 - 2241 vol.4
Main Authors Mei-Ping Song, Guo-Chang Gu
Format Conference Proceeding
LanguageEnglish
Published IEEE 2004
Subjects
Birds
Computational modeling
Computer science
Educational institutions
Equations
Evolutionary computation
Neural networks
Particle swarm optimization
Space technology
System identification
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Summary:Particle swarm optimization (PSO) explores global optimal solution through exploiting the particle's memory and the swarm's memory. Its properties of low constraint on the continuity of objective function and joint of search space, and ability of adapting to dynamic environment make PSO become one of the most important swarm intelligence methods and evolutionary computation algorithms. The fundamental and standard algorithm is introduced firstly. Then the work on the algorithm improvement during the past years is surveyed, as well as the applications on the multi-objective optimization, neural networks and electronics, etc. Finally, the problems remaining unresolved and some directions of PSO research are discussed.
ISBN:0780384032
9780780384033
DOI:10.1109/ICMLC.2004.1382171