Segment-based predominant learning swarm optimizer for large-scale optimization

Qiang Yang, Wei Neng Chen, Tianlong Gu, Huaxiang Zhang, Jeremiah D. Deng, Yun Li, Jun Zhang

Research output: Contribution to journalArticle

27 Citations (Scopus)

Abstract

Large-scale optimization has become a significant yet challenging area in evolutionary computation. To solve this problem, this paper proposes a novel segment-based predominant learning swarm optimizer (SPLSO) swarm optimizer through letting several predominant particles guide the learning of a particle. First, a segment-based learning strategy is proposed to randomly divide the whole dimensions into segments. During update, variables in different segments are evolved by learning from different exemplars while the ones in the same segment are evolved by the same exemplar. Second, to accelerate search speed and enhance search diversity, a predominant learning strategy is also proposed, which lets several predominant particles guide the update of a particle with each predominant particle responsible for one segment of dimensions. By combining these two learning strategies together, SPLSO evolves all dimensions simultaneously and possesses competitive exploration and exploitation abilities. Extensive experiments are conducted on two large-scale benchmark function sets to investigate the influence of each algorithmic component and comparisons with several state-of-the-art meta-heuristic algorithms dealing with large-scale problems demonstrate the competitive efficiency and effectiveness of the proposed optimizer. Further the scalability of the optimizer to solve problems with dimensionality up to 2000 is also verified.

LanguageEnglish
Article number7637019
Pages2896-2910
Number of pages15
JournalIEEE Transactions on Cybernetics
Volume47
Issue number9
Early online date26 Oct 2016
DOIs
Publication statusPublished - 1 Sep 2017

Fingerprint

Heuristic algorithms
Evolutionary algorithms
Scalability
Experiments

Keywords

  • global numerical optimization
  • large-scale optimization
  • particle swarm optimization (PSO)
  • segment-based predominant learning swarm optimizer (SPLSO)

Cite this

Yang, Q., Chen, W. N., Gu, T., Zhang, H., Deng, J. D., Li, Y., & Zhang, J. (2017). Segment-based predominant learning swarm optimizer for large-scale optimization. IEEE Transactions on Cybernetics, 47(9), 2896-2910. [7637019]. https://doi.org/10.1109/TCYB.2016.2616170
Yang, Qiang ; Chen, Wei Neng ; Gu, Tianlong ; Zhang, Huaxiang ; Deng, Jeremiah D. ; Li, Yun ; Zhang, Jun. / Segment-based predominant learning swarm optimizer for large-scale optimization. In: IEEE Transactions on Cybernetics. 2017 ; Vol. 47, No. 9. pp. 2896-2910.
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Yang, Q, Chen, WN, Gu, T, Zhang, H, Deng, JD, Li, Y & Zhang, J 2017, 'Segment-based predominant learning swarm optimizer for large-scale optimization' IEEE Transactions on Cybernetics, vol. 47, no. 9, 7637019, pp. 2896-2910. https://doi.org/10.1109/TCYB.2016.2616170

Segment-based predominant learning swarm optimizer for large-scale optimization. / Yang, Qiang; Chen, Wei Neng; Gu, Tianlong; Zhang, Huaxiang; Deng, Jeremiah D.; Li, Yun; Zhang, Jun.

In: IEEE Transactions on Cybernetics, Vol. 47, No. 9, 7637019, 01.09.2017, p. 2896-2910.

Research output: Contribution to journalArticle

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