A Parallel Strategy Applied to APSO

Qing-Wei Chai; Jeng-Shyang Pan; Wei-Min Zheng; Shu-Chuan Chu

doi:10.1007/978-981-15-3308-2_7

A Parallel Strategy Applied to APSO

Qing-Wei Chai, Jeng-Shyang Pan, Wei-Min Zheng, Shu-Chuan Chu

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

Abstract

Particle Swarm Optimization (PSO) is a famous and effective branch of evolutionary computation, which aims at tackling complex optimization problems. Parallel strategy is an excellent method which separate the population into some subgroups, the subgroups can communicate with each other to improve algorithms’ performance significantly. In this paper, we apply a parallel method on Adaptive Particle Swarm Optimization (APSO), to further improve convergence speed and global search ability of Parallel PSO. The novel Parallel APSO algorithm was verified under many benchmarks of the Congress on Evolutionary Computation (CEC) Competition test suites on real-parameter single-objective optimization and the experimental results showed the proposed Parallel APSO algorithm was competitive with the Parallel PSO.

Original language	English
Title of host publication	Genetic and Evolutionary Computing
Subtitle of host publication	Proceedings of the 13th International Conference on Genetic and Evolutionary Computing, 2019
Editors	Jeng-Shyang Pan, Jerry Chun-Wei Lin, Yongquan Liang, Shu-Chuan Chu
Place of Publication	Singapore
Publisher	Springer
Pages	61-68
Number of pages	8
Volume	1107
ISBN (Electronic)	9789811533082
ISBN (Print)	9789811533075
DOIs	https://doi.org/10.1007/978-981-15-3308-2_7
Publication status	Published - 1 Jan 2020
Externally published	Yes

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1107 AISC
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Keywords

APSO
Parallel APSO
Parallel PSO

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10.1007/978-981-15-3308-2_7Licence: In Copyright

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Chai, Q-W., Pan, J-S., Zheng, W-M., & Chu, S-C. (2020). A Parallel Strategy Applied to APSO. In J-S. Pan, J. C-W. Lin, Y. Liang, & S-C. Chu (Eds.), Genetic and Evolutionary Computing: Proceedings of the 13th International Conference on Genetic and Evolutionary Computing, 2019 (Vol. 1107, pp. 61-68). (Advances in Intelligent Systems and Computing; Vol. 1107 AISC). Springer . https://doi.org/10.1007/978-981-15-3308-2_7

Chai, Qing-Wei ; Pan, Jeng-Shyang ; Zheng, Wei-Min et al. / A Parallel Strategy Applied to APSO. Genetic and Evolutionary Computing: Proceedings of the 13th International Conference on Genetic and Evolutionary Computing, 2019. editor / Jeng-Shyang Pan ; Jerry Chun-Wei Lin ; Yongquan Liang ; Shu-Chuan Chu. Vol. 1107 Singapore : Springer , 2020. pp. 61-68 (Advances in Intelligent Systems and Computing).

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abstract = "Particle Swarm Optimization (PSO) is a famous and effective branch of evolutionary computation, which aims at tackling complex optimization problems. Parallel strategy is an excellent method which separate the population into some subgroups, the subgroups can communicate with each other to improve algorithms{\textquoteright} performance significantly. In this paper, we apply a parallel method on Adaptive Particle Swarm Optimization (APSO), to further improve convergence speed and global search ability of Parallel PSO. The novel Parallel APSO algorithm was verified under many benchmarks of the Congress on Evolutionary Computation (CEC) Competition test suites on real-parameter single-objective optimization and the experimental results showed the proposed Parallel APSO algorithm was competitive with the Parallel PSO.",

keywords = "APSO, Parallel APSO, Parallel PSO",

author = "Qing-Wei Chai and Jeng-Shyang Pan and Wei-Min Zheng and Shu-Chuan Chu",

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Chai, Q-W, Pan, J-S, Zheng, W-M & Chu, S-C 2020, A Parallel Strategy Applied to APSO. in J-S Pan, JC-W Lin, Y Liang & S-C Chu (eds), Genetic and Evolutionary Computing: Proceedings of the 13th International Conference on Genetic and Evolutionary Computing, 2019. vol. 1107, Advances in Intelligent Systems and Computing, vol. 1107 AISC, Springer , Singapore, pp. 61-68. https://doi.org/10.1007/978-981-15-3308-2_7

A Parallel Strategy Applied to APSO. / Chai, Qing-Wei; Pan, Jeng-Shyang; Zheng, Wei-Min et al.

Genetic and Evolutionary Computing: Proceedings of the 13th International Conference on Genetic and Evolutionary Computing, 2019. ed. / Jeng-Shyang Pan; Jerry Chun-Wei Lin; Yongquan Liang; Shu-Chuan Chu. Vol. 1107 Singapore : Springer , 2020. p. 61-68 (Advances in Intelligent Systems and Computing; Vol. 1107 AISC).

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

TY - CHAP

T1 - A Parallel Strategy Applied to APSO

AU - Chai, Qing-Wei

AU - Pan, Jeng-Shyang

AU - Zheng, Wei-Min

AU - Chu, Shu-Chuan

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N2 - Particle Swarm Optimization (PSO) is a famous and effective branch of evolutionary computation, which aims at tackling complex optimization problems. Parallel strategy is an excellent method which separate the population into some subgroups, the subgroups can communicate with each other to improve algorithms’ performance significantly. In this paper, we apply a parallel method on Adaptive Particle Swarm Optimization (APSO), to further improve convergence speed and global search ability of Parallel PSO. The novel Parallel APSO algorithm was verified under many benchmarks of the Congress on Evolutionary Computation (CEC) Competition test suites on real-parameter single-objective optimization and the experimental results showed the proposed Parallel APSO algorithm was competitive with the Parallel PSO.

AB - Particle Swarm Optimization (PSO) is a famous and effective branch of evolutionary computation, which aims at tackling complex optimization problems. Parallel strategy is an excellent method which separate the population into some subgroups, the subgroups can communicate with each other to improve algorithms’ performance significantly. In this paper, we apply a parallel method on Adaptive Particle Swarm Optimization (APSO), to further improve convergence speed and global search ability of Parallel PSO. The novel Parallel APSO algorithm was verified under many benchmarks of the Congress on Evolutionary Computation (CEC) Competition test suites on real-parameter single-objective optimization and the experimental results showed the proposed Parallel APSO algorithm was competitive with the Parallel PSO.

KW - APSO

KW - Parallel APSO

KW - Parallel PSO

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M3 - Chapter

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VL - 1107

T3 - Advances in Intelligent Systems and Computing

SP - 61

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BT - Genetic and Evolutionary Computing

A2 - Pan, Jeng-Shyang

A2 - Lin, Jerry Chun-Wei

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Chai Q-W, Pan J-S, Zheng W-M, Chu S-C. A Parallel Strategy Applied to APSO. In Pan J-S, Lin JC-W, Liang Y, Chu S-C, editors, Genetic and Evolutionary Computing: Proceedings of the 13th International Conference on Genetic and Evolutionary Computing, 2019. Vol. 1107. Singapore: Springer . 2020. p. 61-68. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-981-15-3308-2_7

A Parallel Strategy Applied to APSO

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