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Parallel quantum-behaved particle swarm optimization

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Abstract

Quantum-behaved particle swarm optimization (QPSO), like other population-based algorithms, is intrinsically parallel. The master–slave (synchronous and asynchronous) and static subpopulation parallel QPSO models are investigated and applied to solve the inverse heat conduction problem of identifying the unknown boundary shape. The performance of all these parallel models is compared. The synchronous parallel QPSO can obtain better solutions, while the asynchronous parallel QPSO converges fast without idle waiting. The scalability of the static subpopulation parallel QPSO is not as good as the master–slave parallel model.

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Acknowledgments

This work is partially supported by Bursary of the University of Greenwich, “The Fundamental Research Funds for the Central Universities” (Project Number: JUSRP21012), the innovative research team project of Jiangnan University (Project Number: JNIRT0702), and National Natural Science Foundation of China (Project Number: 60973094).

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Authors and Affiliations

  1. Department of Educational Technology, Jiangnan University, Wuxi, 214122, China

    Na Tian

  2. School of Computing and Mathematical Science, University of Greenwich, London, SE10 9LS, UK

    Choi-Hong Lai

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  1. Na Tian
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  2. Choi-Hong Lai
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Correspondence to Na Tian.

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Tian, N., Lai, CH. Parallel quantum-behaved particle swarm optimization. Int. J. Mach. Learn. & Cyber. 5, 309–318 (2014). https://doi.org/10.1007/s13042-013-0168-2

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  • DOI: https://doi.org/10.1007/s13042-013-0168-2

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