A double particle swarm optimization for mixed-variable optimization problems

Chaoli Sun; Jianchao Zeng; Jengshyang Pan; Shuchuan Chu; Yunqiang Zhang

doi:10.1007/978-3-642-23418-7_9

A double particle swarm optimization for mixed-variable optimization problems

Chaoli Sun, Jianchao Zeng, Jengshyang Pan, Shuchuan Chu, Yunqiang Zhang

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

Abstract

A double particle swarm optimization (DPSO), in which MPSO proposed by Sun et al. [1] is used as a global search algorithm and PSO with feasibility-based rules is used to do local searching, is proposed in this paper to solve mixed-variable optimization problems. MPSO can solve the non-continuous variables very well. However, the imprecise values of continuous variables brought the inconsistent results of each run. A particle swarm optimization with feasibility-based rules is proposed to find optimal values of continuous variables after the MPSO algorithm finishes each independent run, in order to obtain the consistent optimal results for mixed-variable optimization problems. The performance of DPSO is evaluated against two real-world mixed-variable optimization problems, and it is found to be highly competitive compared with other existing algorithms.

Original language	English
Title of host publication	Semantic Methods for Knowledge Management and Communication
Editors	Radoslaw Katarzyniak, Ngoc Thanh Nguyen, Tzu-Fu Chiu, Chao-Fu Hong
Place of Publication	Berlin
Pages	93-102
Number of pages	10
Volume	381
ISBN (Electronic)	9783642234187
DOIs	https://doi.org/10.1007/978-3-642-23418-7_9
Publication status	Published - 7 Sept 2011

Publication series

Name	Studies in Computational Intelligence
Volume	381
ISSN (Print)	1860-949X

Keywords

feasibility-based rules
mixed-variable optimization problems
Particle swarm optimization

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10.1007/978-3-642-23418-7_9Licence: In Copyright

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Sun, C., Zeng, J., Pan, J., Chu, S., & Zhang, Y. (2011). A double particle swarm optimization for mixed-variable optimization problems. In R. Katarzyniak, N. T. Nguyen, T.-F. Chiu, & C.-F. Hong (Eds.), Semantic Methods for Knowledge Management and Communication (Vol. 381, pp. 93-102). (Studies in Computational Intelligence; Vol. 381).. https://doi.org/10.1007/978-3-642-23418-7_9

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title = "A double particle swarm optimization for mixed-variable optimization problems",

abstract = "A double particle swarm optimization (DPSO), in which MPSO proposed by Sun et al. [1] is used as a global search algorithm and PSO with feasibility-based rules is used to do local searching, is proposed in this paper to solve mixed-variable optimization problems. MPSO can solve the non-continuous variables very well. However, the imprecise values of continuous variables brought the inconsistent results of each run. A particle swarm optimization with feasibility-based rules is proposed to find optimal values of continuous variables after the MPSO algorithm finishes each independent run, in order to obtain the consistent optimal results for mixed-variable optimization problems. The performance of DPSO is evaluated against two real-world mixed-variable optimization problems, and it is found to be highly competitive compared with other existing algorithms.",

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author = "Chaoli Sun and Jianchao Zeng and Jengshyang Pan and Shuchuan Chu and Yunqiang Zhang",

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A double particle swarm optimization for mixed-variable optimization problems. / Sun, Chaoli; Zeng, Jianchao; Pan, Jengshyang et al.
Semantic Methods for Knowledge Management and Communication. ed. / Radoslaw Katarzyniak; Ngoc Thanh Nguyen; Tzu-Fu Chiu; Chao-Fu Hong. Vol. 381 Berlin, 2011. p. 93-102 (Studies in Computational Intelligence; Vol. 381).

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

TY - CHAP

T1 - A double particle swarm optimization for mixed-variable optimization problems

AU - Sun, Chaoli

AU - Zeng, Jianchao

AU - Pan, Jengshyang

AU - Chu, Shuchuan

AU - Zhang, Yunqiang

PY - 2011/9/7

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AB - A double particle swarm optimization (DPSO), in which MPSO proposed by Sun et al. [1] is used as a global search algorithm and PSO with feasibility-based rules is used to do local searching, is proposed in this paper to solve mixed-variable optimization problems. MPSO can solve the non-continuous variables very well. However, the imprecise values of continuous variables brought the inconsistent results of each run. A particle swarm optimization with feasibility-based rules is proposed to find optimal values of continuous variables after the MPSO algorithm finishes each independent run, in order to obtain the consistent optimal results for mixed-variable optimization problems. The performance of DPSO is evaluated against two real-world mixed-variable optimization problems, and it is found to be highly competitive compared with other existing algorithms.

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KW - Particle swarm optimization

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VL - 381

T3 - Studies in Computational Intelligence

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A double particle swarm optimization for mixed-variable optimization problems

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