Particle Swarm Optimization Programming

Xiaojun Wu; Ming Zhao; Yaohong Qu

doi:10.1109/CASoN.2010.96

Particle Swarm Optimization Programming

Xiaojun Wu, Ming Zhao, Yaohong Qu

School of Automation

Northwestern Polytechnical University Xian

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

3 Scopus citations

Abstract

PSO is a parallel stochastic optimization algorithm with advantages of less parameters and high efficiency. This paper describes the programming problem in the method of two linear tables with discrete and continuous quantity, then uses discrete PSO algorithm to discrete optimization and continuous PSO to optimize continuous quantity in the solving process respectively, based on these proposes the Particle Swarm Optimization Programming algorithm. Finally, GP and PSOP algorithms are compared by applying them to solving programming problem respectively with three typical test functions, the results show that the PSOP algorithm has better convergence precision and stability than the GP algorithm.

Original language	English
Title of host publication	Proceedings - International Conference on Computational Aspects of Social Networks, CASoN'10
Pages	397-400
Number of pages	4
DOIs	https://doi.org/10.1109/CASoN.2010.96
State	Published - 2010
Event	International Conference on Computational Aspects of Social Networks, CASoN'10 - Taiyuan, China Duration: 26 Sep 2010 → 28 Sep 2010

Publication series

Name	Proceedings - International Conference on Computational Aspects of Social Networks, CASoN'10

Conference

Conference	International Conference on Computational Aspects of Social Networks, CASoN'10
Country/Territory	China
City	Taiyuan
Period	26/09/10 → 28/09/10

Keywords

GP algorithm
PSO
Two linear tables

Access to Document

10.1109/CASoN.2010.96

Cite this

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title = "Particle Swarm Optimization Programming",

abstract = "PSO is a parallel stochastic optimization algorithm with advantages of less parameters and high efficiency. This paper describes the programming problem in the method of two linear tables with discrete and continuous quantity, then uses discrete PSO algorithm to discrete optimization and continuous PSO to optimize continuous quantity in the solving process respectively, based on these proposes the Particle Swarm Optimization Programming algorithm. Finally, GP and PSOP algorithms are compared by applying them to solving programming problem respectively with three typical test functions, the results show that the PSOP algorithm has better convergence precision and stability than the GP algorithm.",

keywords = "GP algorithm, PSO, Two linear tables",

author = "Xiaojun Wu and Ming Zhao and Yaohong Qu",

year = "2010",

doi = "10.1109/CASoN.2010.96",

language = "英语",

isbn = "9780769542027",

series = "Proceedings - International Conference on Computational Aspects of Social Networks, CASoN'10",

pages = "397--400",

booktitle = "Proceedings - International Conference on Computational Aspects of Social Networks, CASoN'10",

note = "International Conference on Computational Aspects of Social Networks, CASoN'10 ; Conference date: 26-09-2010 Through 28-09-2010",

}

Wu, X, Zhao, M & Qu, Y 2010, Particle Swarm Optimization Programming. in Proceedings - International Conference on Computational Aspects of Social Networks, CASoN'10., 5636594, Proceedings - International Conference on Computational Aspects of Social Networks, CASoN'10, pp. 397-400, International Conference on Computational Aspects of Social Networks, CASoN'10, Taiyuan, China, 26/09/10. https://doi.org/10.1109/CASoN.2010.96

Particle Swarm Optimization Programming. / Wu, Xiaojun; Zhao, Ming; Qu, Yaohong.
Proceedings - International Conference on Computational Aspects of Social Networks, CASoN'10. 2010. p. 397-400 5636594 (Proceedings - International Conference on Computational Aspects of Social Networks, CASoN'10).

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

TY - GEN

T1 - Particle Swarm Optimization Programming

AU - Wu, Xiaojun

AU - Zhao, Ming

AU - Qu, Yaohong

PY - 2010

Y1 - 2010

N2 - PSO is a parallel stochastic optimization algorithm with advantages of less parameters and high efficiency. This paper describes the programming problem in the method of two linear tables with discrete and continuous quantity, then uses discrete PSO algorithm to discrete optimization and continuous PSO to optimize continuous quantity in the solving process respectively, based on these proposes the Particle Swarm Optimization Programming algorithm. Finally, GP and PSOP algorithms are compared by applying them to solving programming problem respectively with three typical test functions, the results show that the PSOP algorithm has better convergence precision and stability than the GP algorithm.

AB - PSO is a parallel stochastic optimization algorithm with advantages of less parameters and high efficiency. This paper describes the programming problem in the method of two linear tables with discrete and continuous quantity, then uses discrete PSO algorithm to discrete optimization and continuous PSO to optimize continuous quantity in the solving process respectively, based on these proposes the Particle Swarm Optimization Programming algorithm. Finally, GP and PSOP algorithms are compared by applying them to solving programming problem respectively with three typical test functions, the results show that the PSOP algorithm has better convergence precision and stability than the GP algorithm.

KW - GP algorithm

KW - PSO

KW - Two linear tables

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U2 - 10.1109/CASoN.2010.96

DO - 10.1109/CASoN.2010.96

M3 - 会议稿件

AN - SCOPUS:78650368676

SN - 9780769542027

T3 - Proceedings - International Conference on Computational Aspects of Social Networks, CASoN'10

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EP - 400

BT - Proceedings - International Conference on Computational Aspects of Social Networks, CASoN'10

T2 - International Conference on Computational Aspects of Social Networks, CASoN'10

Y2 - 26 September 2010 through 28 September 2010

ER -

Particle Swarm Optimization Programming

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Keywords

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