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Citation: Haoran LIU, Liyue ZHANG, Ruixing FAN, Haiyu WANG, Chunlan ZHANG. Bayesian Network Structure Learning Based on Improved Whale Optimization Strategy[J]. Journal of Electronics and Information Technology, ;2019, 41(6): 1434-1441. doi: 10.11999/JEIT180653 shu

Bayesian Network Structure Learning Based on Improved Whale Optimization Strategy

  • Corresponding author: Haoran LIU, liu.haoran@ysu.edu.cn
  • Received Date: 2018-07-03
    Accepted Date: 2019-01-15
    Available Online: 2019-06-01

Figures(3) / Tables(3)

  • A Bayesian network structure learning algorithm based on improved whale optimization strategy is proposed to solve the problem that the current Bayesian network structure learning algorithm is easily trapped in local optimal and is of low optimization efficiency. The improved algorithm proposes first a new method to establish a better initial population, and then it uses the cross mutation operator that does not produce the illegal structure to construct an improved predation behavior suitable for Bayesian network structure learning. At the same time, it adopts the dynamic parameter tuning strategy to enhance the individual search ability. The population is updated followed by the fitness order so that the optimal Bayesian network structure is obtained. Simulation results demonstrate that the algorithm has global convergence, high efficiency and higher accuracy than other similar optimization algorithms.
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