Adaptive particle swarm optimization algorithm

Tao Cai; Feng Pan; Jie Chen

Adaptive particle swarm optimization algorithm

Tao Cai^*, Feng Pan, Jie Chen

^*Corresponding author for this work

School of Automation

Beijing Institute of Technology

Research output: Contribution to conference › Paper › peer-review

7 Citations (Scopus)

Abstract

The particle swarm optimization (PSO) has exhibited good performance on optimization. However, the parameters, which greatly influence the algorithm stability and performance, are selected depending on experience of designer. The selection of parameters needs to consider both the convergence and avoiding premature convergence. Adaptive PSO (APSO) was presented, based on the stability criterion of the PSO as a time-varying discrete system. Simulation results of some well-known problems show that APSO not only ensure the stability of algorithm, but also avoid premature convergence effectively and clearly outperform the standard PSO.

Original language	English
Pages	2245-2247
Number of pages	3
Publication status	Published - 2004
Event	WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings - Hangzhou, China Duration: 15 Jun 2004 → 19 Jun 2004

Conference

Conference	WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings
Country/Territory	China
City	Hangzhou
Period	15/06/04 → 19/06/04

Keywords

Adaptive particle swarm optimization (APSO)
Premature convergence
Stability

Cite this

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title = "Adaptive particle swarm optimization algorithm",

abstract = "The particle swarm optimization (PSO) has exhibited good performance on optimization. However, the parameters, which greatly influence the algorithm stability and performance, are selected depending on experience of designer. The selection of parameters needs to consider both the convergence and avoiding premature convergence. Adaptive PSO (APSO) was presented, based on the stability criterion of the PSO as a time-varying discrete system. Simulation results of some well-known problems show that APSO not only ensure the stability of algorithm, but also avoid premature convergence effectively and clearly outperform the standard PSO.",

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author = "Tao Cai and Feng Pan and Jie Chen",

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AU - Cai, Tao

AU - Pan, Feng

AU - Chen, Jie

PY - 2004

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N2 - The particle swarm optimization (PSO) has exhibited good performance on optimization. However, the parameters, which greatly influence the algorithm stability and performance, are selected depending on experience of designer. The selection of parameters needs to consider both the convergence and avoiding premature convergence. Adaptive PSO (APSO) was presented, based on the stability criterion of the PSO as a time-varying discrete system. Simulation results of some well-known problems show that APSO not only ensure the stability of algorithm, but also avoid premature convergence effectively and clearly outperform the standard PSO.

AB - The particle swarm optimization (PSO) has exhibited good performance on optimization. However, the parameters, which greatly influence the algorithm stability and performance, are selected depending on experience of designer. The selection of parameters needs to consider both the convergence and avoiding premature convergence. Adaptive PSO (APSO) was presented, based on the stability criterion of the PSO as a time-varying discrete system. Simulation results of some well-known problems show that APSO not only ensure the stability of algorithm, but also avoid premature convergence effectively and clearly outperform the standard PSO.

KW - Adaptive particle swarm optimization (APSO)

KW - Premature convergence

KW - Stability

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M3 - Paper

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T2 - WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings

Y2 - 15 June 2004 through 19 June 2004

ER -

Adaptive particle swarm optimization algorithm

Abstract

Conference

Keywords

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