Bayesian Network Structure Learning Based on Improved Whale Optimization Strategy

Haoran LIU; Liyue ZHANG; Ruixing FAN; Haiyu WANG; Chunlan ZHANG

doi:10.11999/JEIT180653

Volume 41 Issue 6

Jun. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(6): 1434-1441

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

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 & Information Technology, 2019, 41(6): 1434-1441. doi: 10.11999/JEIT180653

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Bayesian Network Structure Learning Based on Improved Whale Optimization Strategy

doi: 10.11999/JEIT180653 cstr: 32379.14.JEIT180653

1.
School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
2.
The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, Yanshan University, Qinhuangdao 066004, China

Funds: The National Natural Science Foundation of China (51641609)

Received Date: 2018-07-03
Rev Recd Date: 2019-01-15

Available Online: 2019-01-26

Publish Date: 2019-06-01

Abstract

Abstract

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.
- Bayesian network structure learning,
- Improved whale optimization algorithm,
- Improved hunt behavior,
- Dynamic adjustment parameter

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References(17)

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