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 proceedingChapter

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 languageEnglish
Title of host publicationGenetic and Evolutionary Computing - Proceedings of the 13th International Conference on Genetic and Evolutionary Computing, 2019
EditorsJeng-Shyang Pan, Jerry Chun-Wei Lin, Yongquan Liang, Shu-Chuan Chu
Place of PublicationQingdao, China
PublisherSpringer Singapore
Pages61-68
Number of pages8
Volume1107
ISBN (Print)978-981-15-3307-5
DOIs
Publication statusPublished - 1 Jan 2020
Externally publishedYes

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1107 AISC
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Keywords

  • APSO
  • Parallel APSO
  • Parallel PSO

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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 Singapore. https://doi.org/10.1007/978-981-15-3308-2_7