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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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    Cited by:

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    3. 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.

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