基于改进鲸鱼优化策略的贝叶斯网络结构学习算法

Bayesian Network Structure Learning Based on Improved Whale Optimization Strategy

针对当前贝叶斯网络结构学习算法易陷入局部最优和寻优效率低的问题,该文提出一种基于改进鲸鱼优化策略的贝叶斯网络结构学习算法。该算法首先提出一种新的方法建立较优的初始种群,然后利用不产生非法结构的交叉变异算子构建适用于贝叶斯网络结构学习的改进捕食行为,同时采用动态调节参数增强算法个体寻优的能力,通过适应度排序更新种群,最终获得最优的贝叶斯网络结构。仿真结果表明,该算法具有全局收敛性,寻优效率高,精确率高于其它同类优化算法。

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.