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A quantum particle swarm optimizer with chaotic mutation operator

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  • Coelho, Leandro dos Santos

Abstract

Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm that shares many similarities with evolutionary computation techniques. However, the PSO is driven by the simulation of a social psychological metaphor motivated by collective behaviors of bird and other social organisms instead of the survival of the fittest individual. Inspired by the classical PSO method and quantum mechanics theories, this work presents a novel Quantum-behaved PSO (QPSO) using chaotic mutation operator. The application of chaotic sequences based on chaotic Zaslavskii map instead of random sequences in QPSO is a powerful strategy to diversify the QPSO population and improve the QPSO’s performance in preventing premature convergence to local minima. The simulation results demonstrate good performance of the QPSO in solving a well-studied continuous optimization problem of mechanical engineering design.

Suggested Citation

  • Coelho, Leandro dos Santos, 2008. "A quantum particle swarm optimizer with chaotic mutation operator," Chaos, Solitons & Fractals, Elsevier, vol. 37(5), pages 1409-1418.
    • Handle: RePEc:eee:chsofr:v:37:y:2008:i:5:p:1409-1418
    DOI: 10.1016/j.chaos.2006.10.028
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    References listed on IDEAS

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    1. Liu, Bo & Wang, Ling & Jin, Yi-Hui & Tang, Fang & Huang, De-Xian, 2006. "Directing orbits of chaotic systems by particle swarm optimization," Chaos, Solitons & Fractals, Elsevier, vol. 29(2), pages 454-461.
    2. Li, Lixiang & Yang, Yixian & Peng, Haipeng & Wang, Xiangdong, 2006. "Parameters identification of chaotic systems via chaotic ant swarm," Chaos, Solitons & Fractals, Elsevier, vol. 28(5), pages 1204-1211.
    3. Yan, Jun-Juh & Hung, Meei-Ling, 2006. "A novel stability criterion for interval time-delay chaotic systems via the evolutionary programming approach," Chaos, Solitons & Fractals, Elsevier, vol. 29(5), pages 1079-1084.
    4. D. Bulger & W. P. Baritompa & G. R. Wood, 2003. "Implementing Pure Adaptive Search with Grover's Quantum Algorithm," Journal of Optimization Theory and Applications, Springer, vol. 116(3), pages 517-529, March.
    5. Chang, Wei-Der, 2006. "Parameter identification of Rossler’s chaotic system by an evolutionary algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 29(5), pages 1047-1053.
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    Cited by:

    1. Jiao, Bin & Lian, Zhigang & Chen, Qunxian, 2009. "A dynamic global and local combined particle swarm optimization algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 42(5), pages 2688-2695.
    2. Ricardo Faia & Tiago Pinto & Zita Vale & Juan Manuel Corchado, 2017. "An Ad-Hoc Initial Solution Heuristic for Metaheuristic Optimization of Energy Market Participation Portfolios," Energies, MDPI, vol. 10(7), pages 1-18, June.
    3. Hadi Mokhtari & Amir Noroozi, 2018. "An efficient chaotic based PSO for earliness/tardiness optimization in a batch processing flow shop scheduling problem," Journal of Intelligent Manufacturing, Springer, vol. 29(5), pages 1063-1081, June.

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