The multiple population co-evolution PSO algorithm

Xuan Xiao; Qianqian Zhang

doi:10.1007/978-3-319-11897-0_49

The multiple population co-evolution PSO algorithm

Xuan Xiao, Qianqian Zhang

School of Automation

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

In order to overcome the standard particle swarm optimization algorithm which is easily trapped in local minima and optimize the shortcoming of low precision, this paper proposed a way which can make multiple information exchange between particles come true: the multiple population co-evolution PSO algorithm. This paper proposes a multiple population co-evolutionary algorithm to achieve communication among populations, and then show the feasibility and effectiveness of this algorithm through experiments.

Original language	English
Title of host publication	Advances in Swarm Intelligence - 5th International Conference, ICSI 2014, Proceedings
Editors	Ying Tan, Yuhui Shi, Carlos A. Coello Coello
Publisher	Springer Verlag
Pages	434-441
Number of pages	8
ISBN (Electronic)	9783319118963
DOIs	https://doi.org/10.1007/978-3-319-11897-0_49
Publication status	Published - 2014
Event	5th International Conference on Advances in Swarm Intelligence, ICSI 2014 - Hefei, China Duration: 17 Oct 2014 → 20 Oct 2014

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8795
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th International Conference on Advances in Swarm Intelligence, ICSI 2014
Country/Territory	China
City	Hefei
Period	17/10/14 → 20/10/14

Keywords

Co-evolution
PSO multiple population
Particle swarm

Access to Document

10.1007/978-3-319-11897-0_49

Cite this

Xiao, X., & Zhang, Q. (2014). The multiple population co-evolution PSO algorithm. In Y. Tan, Y. Shi, & C. A. C. Coello (Eds.), Advances in Swarm Intelligence - 5th International Conference, ICSI 2014, Proceedings (pp. 434-441). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8795). Springer Verlag. https://doi.org/10.1007/978-3-319-11897-0_49

Xiao, Xuan ; Zhang, Qianqian. / The multiple population co-evolution PSO algorithm. Advances in Swarm Intelligence - 5th International Conference, ICSI 2014, Proceedings. editor / Ying Tan ; Yuhui Shi ; Carlos A. Coello Coello. Springer Verlag, 2014. pp. 434-441 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "In order to overcome the standard particle swarm optimization algorithm which is easily trapped in local minima and optimize the shortcoming of low precision, this paper proposed a way which can make multiple information exchange between particles come true: the multiple population co-evolution PSO algorithm. This paper proposes a multiple population co-evolutionary algorithm to achieve communication among populations, and then show the feasibility and effectiveness of this algorithm through experiments.",

keywords = "Co-evolution, PSO multiple population, Particle swarm",

author = "Xuan Xiao and Qianqian Zhang",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014.; 5th International Conference on Advances in Swarm Intelligence, ICSI 2014 ; Conference date: 17-10-2014 Through 20-10-2014",

year = "2014",

doi = "10.1007/978-3-319-11897-0_49",

language = "English",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

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Xiao, X & Zhang, Q 2014, The multiple population co-evolution PSO algorithm. in Y Tan, Y Shi & CAC Coello (eds), Advances in Swarm Intelligence - 5th International Conference, ICSI 2014, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8795, Springer Verlag, pp. 434-441, 5th International Conference on Advances in Swarm Intelligence, ICSI 2014, Hefei, China, 17/10/14. https://doi.org/10.1007/978-3-319-11897-0_49

The multiple population co-evolution PSO algorithm. / Xiao, Xuan; Zhang, Qianqian.
Advances in Swarm Intelligence - 5th International Conference, ICSI 2014, Proceedings. ed. / Ying Tan; Yuhui Shi; Carlos A. Coello Coello. Springer Verlag, 2014. p. 434-441 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8795).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - In order to overcome the standard particle swarm optimization algorithm which is easily trapped in local minima and optimize the shortcoming of low precision, this paper proposed a way which can make multiple information exchange between particles come true: the multiple population co-evolution PSO algorithm. This paper proposes a multiple population co-evolutionary algorithm to achieve communication among populations, and then show the feasibility and effectiveness of this algorithm through experiments.

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Xiao X, Zhang Q. The multiple population co-evolution PSO algorithm. In Tan Y, Shi Y, Coello CAC, editors, Advances in Swarm Intelligence - 5th International Conference, ICSI 2014, Proceedings. Springer Verlag. 2014. p. 434-441. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-11897-0_49

The multiple population co-evolution PSO algorithm

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