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A Novel Distributed Quantum-Behaved Particle Swarm Optimization

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  • Yangyang Li
  • Zhenghan Chen
  • Yang Wang
  • Licheng Jiao
  • Yu Xue

Abstract

Quantum-behaved particle swarm optimization (QPSO) is an improved version of particle swarm optimization (PSO) and has shown superior performance on many optimization problems. But for now, it may not always satisfy the situations. Nowadays, problems become larger and more complex, and most serial optimization algorithms cannot deal with the problem or need plenty of computing cost. Fortunately, as an effective model in dealing with problems with big data which need huge computation, MapReduce has been widely used in many areas. In this paper, we implement QPSO on MapReduce model and propose MapReduce quantum-behaved particle swarm optimization (MRQPSO) which achieves parallel and distributed QPSO. Comparisons are made between MRQPSO and QPSO on some test problems and nonlinear equation systems. The results show that MRQPSO could complete computing task with less time. Meanwhile, from the view of optimization performance, MRQPSO outperforms QPSO in many cases.

Suggested Citation

  • Yangyang Li & Zhenghan Chen & Yang Wang & Licheng Jiao & Yu Xue, 2017. "A Novel Distributed Quantum-Behaved Particle Swarm Optimization," Journal of Optimization, Hindawi, vol. 2017, pages 1-9, May.
    • Handle: RePEc:hin:jjopti:4685923
    DOI: 10.1155/2017/4685923
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    References listed on IDEAS

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    1. Hipp, Christian & Plum, Michael, 2000. "Optimal investment for insurers," Insurance: Mathematics and Economics, Elsevier, vol. 27(2), pages 215-228, October.
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    Cited by:

    1. Tariq Ahamed Ahanger & Fadl Dahan & Usman Tariq & Imdad Ullah, 2022. "Quantum Inspired Task Optimization for IoT Edge Fog Computing Environment," Mathematics, MDPI, vol. 11(1), pages 1-28, December.
    2. Zhaojuan Zhang & Wanliang Wang & Gaofeng Pan, 2020. "A Distributed Quantum-Behaved Particle Swarm Optimization Using Opposition-Based Learning on Spark for Large-Scale Optimization Problem," Mathematics, MDPI, vol. 8(11), pages 1-21, October.

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