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| Bayesian Network Structure Learning for Node Order Optimization Based on Improved Genetic-Wolf Pack Algorithm |
| LIU Hao-ran1,2,SU Zhao-yu1,ZHANG Li-yue1,WANG Nian-tai1,FAN Rui-xing1 |
1. Institute of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
2. Hebei Province Key Laboratory of Special Optical Fiber and Optical Fiber Sensing, Qinhuangdao,Hebei 066004, China |
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Abstract Bayesian network is an important method in the field of data mining. The Bayesian network structure learning algorithm is easy to fall into the problem of local optimization and low efficiency. A Bayesian network structure learning algorithm based on improved hybrid genetic wolves group is proposed to optimize the node order. Firstly, the algorithm uses the depth-first search to rank the nodes of the largest supporting tree. Then, using dynamic mutation and optimal crossover operator to construct predator behavior that suitable for node order optimization. The algorithm introduces dynamic parameter factors to enhance the ability of local optimization. Finally, the optimal Bayesian network structure is obtained by combining with K2 algorithm. Experiments are performed on three different sizes of standard network data sets. The simulation results show that the algorithm has high optimization and the optimization efficiency is higher than other similar optimization algorithms.
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Received: 15 December 2020
Published: 13 January 2023
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| Fund:Foundation Items：National Key R&D Program of China:Research on the Key and Integrated Technology of Intelligent Network Collaborative Manufacturing for High-efficiency Precision CNC Machine Tools;Hebei Talent Engineering Training Support Project |
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2023, Vol. 44 
