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An electronic transition-based bare bones particle swarm optimization algorithm for high dimensional optimization problems

Author

Listed:
  • Hao Tian
  • Jia Guo
  • Haiyang Xiao
  • Ke Yan
  • Yuji Sato

Abstract

An electronic transition-based bare bones particle swarm optimization (ETBBPSO) algorithm is proposed in this paper. The ETBBPSO is designed to present high precision results for high dimensional single-objective optimization problems. Particles in the ETBBPSO are divided into different orbits. A transition operator is proposed to enhance the global search ability of ETBBPSO. The transition behavior of particles gives the swarm more chance to escape from local minimums. In addition, an orbit merge operator is proposed in this paper. An orbit with low search ability will be merged by an orbit with high search ability. Extensive experiments with CEC2014 and CEC2020 are evaluated with ETBBPSO. Four famous population-based algorithms are also selected in the control group. Experimental results prove that ETBBPSO can present high precision results for high dimensional single-objective optimization problems.

Suggested Citation

  • Hao Tian & Jia Guo & Haiyang Xiao & Ke Yan & Yuji Sato, 2022. "An electronic transition-based bare bones particle swarm optimization algorithm for high dimensional optimization problems," PLOS ONE, Public Library of Science, vol. 17(7), pages 1-23, July.
    • Handle: RePEc:plo:pone00:0271925
    DOI: 10.1371/journal.pone.0271925
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    References listed on IDEAS

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    1. Xin Zhang & Dexuan Zou & Xin Shen, 2018. "A Novel Simple Particle Swarm Optimization Algorithm for Global Optimization," Mathematics, MDPI, vol. 6(12), pages 1-34, November.
    2. Lai Xu & Aamir Muhammad & Yifei Pu & Jiliu Zhou & Yi Zhang, 2019. "Fractional-order quantum particle swarm optimization," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-16, June.
    3. Xuan Chen & Feng Cheng & Cong Liu & Long Cheng & Yin Mao, 2021. "An improved Wolf pack algorithm for optimization problems: Design and evaluation," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-23, August.
    4. Xuyang Teng & Hongbin Dong & Xiurong Zhou, 2017. "Adaptive feature selection using v-shaped binary particle swarm optimization," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-22, March.
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