A level-based learning swarm optimizer for large-scale optimization

Research output: Research - peer-reviewArticle

  • Qiang Yang
  • Wei-Neng Chen
  • Jeremiah Da Deng
  • Yun Li
  • Tianlong Gu
  • Jun Zhang

In pedagogy, teachers usually separate mixed-level students into different levels, treat them differently and teach them in accordance with their cognitive and learning abilities. Inspired from this idea, we consider particles in the swarm as mixed-level students and propose a level-based learning swarm optimizer (LLSO) to settle large-scale optimization, which is still considerably challenging in evolutionary computation. At first, a level-based learning strategy is introduced, which separates particles into a number of levels according to their fitness values and treats particles in different levels differently. Then, a new exemplar selection strategy is designed to randomly select two predominant particles from two different higher levels in the current swarm to guide the learning of particles. The cooperation between these two strategies could afford great diversity enhancement for the optimizer. Further, the exploration and exploitation abilities of the optimizer are analyzed both theoretically and empirically in comparison with two popular particle swarm optimizers. Extensive comparisons with several state-of-the-art algorithms on two widely used sets of large-scale benchmark functions confirm the competitive performance of the proposed optimizer in both solution quality and computational efficiency. Finally, comparison experiments on problems with dimensionality increasing from 200 to 2000 further substantiate the good scalability of the developed optimizer.

Original languageEnglish
Pages (from-to)578-594
Number of pages17
JournalIEEE Transactions on Evolutionary Computation
Volume22
Issue number4
Early online date5 Sep 2017
DOIs
StateE-pub ahead of print - 5 Sep 2017

    Research areas

  • exemplar selection, high-dimensional problems, large-scale optimization, level-based learning swarm optimizer (LLSO), particle swarm optimization (PSO)

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