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Fractional-order quantum particle swarm optimization

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  • Lai Xu
  • Aamir Muhammad
  • Yifei Pu
  • Jiliu Zhou
  • Yi Zhang

Abstract

Motivated by the concepts of quantum mechanics and particle swarm optimization (PSO), quantum-behaved particle swarm optimization (QPSO) was developed to achieve better global search ability. This paper proposes a new method to improve the global search ability of QPSO with fractional calculus (FC). Based on one of the most frequently used fractional differential definitions, the Grünwald-Letnikov definition, we introduce its discrete expression into the position updating of QPSO. Extensive experiments on well-known benchmark functions were performed to evaluate the performance of the proposed fractional-order quantum particle swarm optimization (FQPSO). The experimental results demonstrate its superior ability in achieving optimal solutions for several different optimizations.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0218285
    DOI: 10.1371/journal.pone.0218285
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    References listed on IDEAS

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    1. B. E. Kane, 1998. "A silicon-based nuclear spin quantum computer," Nature, Nature, vol. 393(6681), pages 133-137, May.
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    Cited by:

    1. Abedi Pahnehkolaei, Seyed Mehdi & Alfi, Alireza & Tenreiro Machado, J.A., 2022. "Analytical stability analysis of the fractional-order particle swarm optimization algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).

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