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 proceedingChapterpeer-review


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
Subtitle of host publicationProceedings 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 PublicationSingapore
Number of pages8
ISBN (Electronic)9789811533082
ISBN (Print)9789811533075
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


  • APSO
  • Parallel APSO
  • Parallel PSO


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