基于混合差分蜂群算法的贝叶斯网络结构学习

Bayesian Network Structure Learning Based on Hybrid Differential Evolution and Bee Colony Algorithm

贝叶斯网络的结构学习是贝叶斯网络理论模型的核心,而现有的贝叶斯网络结构学习算法一般存在效率偏低的问题.针对此问题,文中提出基于混合差分蜂群算法的贝叶斯网络结构学习算法.该算法首先利用最大生成树准则得到初始种群,然后利用差分进化算法中的交叉、变异规则优化初始种群.在使用差分进化算法的过程中,分别将蜂群算法应用于变异阶段和优化改进交叉阶段,并且将云自适应理论应用于选择阶段选择生成个体.在经典贝叶斯网络上的仿真实验证明,文中算法在贝叶斯网络结构学习中具有较强的寻优能力.

Bayesian network structure learning is the core of Bayesian network theory and the current algorithms of learning Bayesian network structures are always inefficient. A method of learning Bayesian network structure based on hybrid differential evolution and bee colony algorithm is proposed. The maximum weight spanning tree is used to generate the candidate networks, and then the differential evolution algorithm is used to optimize the initial populations. In the process of using the differential evolution algorithm, the bee colony algorithm is introduced into variation stage and optiinizing cross stage, and better candidates are selected by applying cloud-based adaptive theory to the choose stage. Simulation results on classic Bayesian network show that the proposed algorithm has a strong searching ability in Bayesian network structure learning.