Generalized particle swarm optimizers with tracking multiple local optima for multimodal functions optimization

Haijun Zhang; Tommy W. S. Chow; Anthony Fong

doi:10.1109/ICC.2009.47

Generalized particle swarm optimizers with tracking multiple local optima for multimodal functions optimization

Haijun Zhang^*
, Tommy W. S. Chow
, Anthony Fong

^*Corresponding author for this work

Department of Electrical Engineering

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

This paper presents a new variation of particle swarm optimization (PSO) algorithm called generalized particle swarm optimizer (GPSO). It extends the basic learning strategy of traditional PSO and exerts the swarms to significantly improve the group learning performance. In this scheme, a particle of PSO in each dimension does not only follow its own local optima, but also follows other superior particles' local optima with creditability. Based on our experimental verifications, the results suggest that GPSO delivers superior performance for multimodal functions optimization compared with the state-of-art PSO methods. © 2009 IEEE.

Original language	English
Title of host publication	ICC2009 - International Conference of Computing in Engineering, Science and Information
Pages	213-216
DOIs	https://doi.org/10.1109/ICC.2009.47
Publication status	Published - 2009
Event	ICC2009 - International Conference of Computing in Engineering, Science and Information - Fullerton, CA, United States Duration: 2 Apr 2009 → 4 Apr 2009

Conference

Conference	ICC2009 - International Conference of Computing in Engineering, Science and Information
Place	United States
City	Fullerton, CA
Period	2/04/09 → 4/04/09

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10.1109/ICC.2009.47

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@inproceedings{ddad485d1a924c4c863c08c46b8bed9f,

title = "Generalized particle swarm optimizers with tracking multiple local optima for multimodal functions optimization",

abstract = "This paper presents a new variation of particle swarm optimization (PSO) algorithm called generalized particle swarm optimizer (GPSO). It extends the basic learning strategy of traditional PSO and exerts the swarms to significantly improve the group learning performance. In this scheme, a particle of PSO in each dimension does not only follow its own local optima, but also follows other superior particles' local optima with creditability. Based on our experimental verifications, the results suggest that GPSO delivers superior performance for multimodal functions optimization compared with the state-of-art PSO methods. {\textcopyright} 2009 IEEE.",

author = "Haijun Zhang and Chow, \{Tommy W. S.\} and Anthony Fong",

year = "2009",

doi = "10.1109/ICC.2009.47",

language = "English",

isbn = "9780769535388",

pages = "213--216",

booktitle = "ICC2009 - International Conference of Computing in Engineering, Science and Information",

note = "ICC2009 - International Conference of Computing in Engineering, Science and Information ; Conference date: 02-04-2009 Through 04-04-2009",

}

Zhang, H, Chow, TWS & Fong, A 2009, Generalized particle swarm optimizers with tracking multiple local optima for multimodal functions optimization. in ICC2009 - International Conference of Computing in Engineering, Science and Information., 5328132, pp. 213-216, ICC2009 - International Conference of Computing in Engineering, Science and Information, Fullerton, CA, United States, 2/04/09. https://doi.org/10.1109/ICC.2009.47

Generalized particle swarm optimizers with tracking multiple local optima for multimodal functions optimization. / Zhang, Haijun; Chow, Tommy W. S.; Fong, Anthony.
ICC2009 - International Conference of Computing in Engineering, Science and Information. 2009. p. 213-216 5328132.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Generalized particle swarm optimizers with tracking multiple local optima for multimodal functions optimization

AU - Zhang, Haijun

AU - Chow, Tommy W. S.

AU - Fong, Anthony

PY - 2009

Y1 - 2009

N2 - This paper presents a new variation of particle swarm optimization (PSO) algorithm called generalized particle swarm optimizer (GPSO). It extends the basic learning strategy of traditional PSO and exerts the swarms to significantly improve the group learning performance. In this scheme, a particle of PSO in each dimension does not only follow its own local optima, but also follows other superior particles' local optima with creditability. Based on our experimental verifications, the results suggest that GPSO delivers superior performance for multimodal functions optimization compared with the state-of-art PSO methods. © 2009 IEEE.

AB - This paper presents a new variation of particle swarm optimization (PSO) algorithm called generalized particle swarm optimizer (GPSO). It extends the basic learning strategy of traditional PSO and exerts the swarms to significantly improve the group learning performance. In this scheme, a particle of PSO in each dimension does not only follow its own local optima, but also follows other superior particles' local optima with creditability. Based on our experimental verifications, the results suggest that GPSO delivers superior performance for multimodal functions optimization compared with the state-of-art PSO methods. © 2009 IEEE.

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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-73449116398&origin=recordpage

U2 - 10.1109/ICC.2009.47

DO - 10.1109/ICC.2009.47

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9780769535388

SP - 213

EP - 216

BT - ICC2009 - International Conference of Computing in Engineering, Science and Information

T2 - ICC2009 - International Conference of Computing in Engineering, Science and Information

Y2 - 2 April 2009 through 4 April 2009

ER -

Generalized particle swarm optimizers with tracking multiple local optima for multimodal functions optimization

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