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Nuclear Energy Spectrum Decomposition Based on Hybrid Particle Swarm Optimization

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  • Xing-Ke Ma
  • Yang-Zhen Ji

Abstract

A nonlinear fitting model is proposed for the problem of nuclear energy spectrum decomposition. And the hybrid particle swarm optimization algorithm based on natural selection idea and random inertia weight is used to solve. First, a nonlinear fitting model was introduced. Secondly, the defects of the traditional particle swarm optimization algorithm based on linear inertia weight are analyzed, and the ideas of stochastic inertia weight and natural selection are integrated into the algorithm for these shortcomings. Then, according to the specific problems involved in this paper and the existing data, the continuous function model is transformed into a discrete series model. According to the nature that the absolute value is not less than zero, the fitness value is appropriately modified to achieve the purpose of improving the calculation accuracy and the operation speed of the algorithm.

Suggested Citation

  • Xing-Ke Ma & Yang-Zhen Ji, 2019. "Nuclear Energy Spectrum Decomposition Based on Hybrid Particle Swarm Optimization," International Journal of Sciences, Office ijSciences, vol. 8(05), pages 135-138, May.
    • Handle: RePEc:adm:journl:v:8:y:2019:i:5:p:135-138
    DOI: 10.18483/ijSci.2075
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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, 2005. "Improved particle swarm optimization combined with chaos," Chaos, Solitons & Fractals, Elsevier, vol. 25(5), pages 1261-1271.
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