基于混合樽海鞘-差分进化算法的贝叶斯网络结构学习算法

Bayesian network structure learning algorithm based on hybrid binary salp swarm-differential evolution algorithm

针对目前利用启发式算法学习贝叶斯网络结构易陷入局部最优、寻优效率低的问题,提出一种基于混合樽海鞘-差分进化算法的贝叶斯网络结构学习算法。该算法在种群划分阶段提出自适应的规模因子平衡局部搜索与全局搜索,在子种群更新阶段利用改进的变异算子与交叉算子构建樽海鞘搜索策略与差分搜索策略,更新不同的子种群,在合并子种群阶段利用两点变异算子增加种群多样性。由算法的收敛性分析可知,通过种群的迭代搜索可以找到最佳结构。实验结果表明,与其他算法相比,所提算法收敛精度与寻优效率均有提升。

Aiming at the disadvantages of Bayesian network structure learned by heuristic algorithms, which were trap- ping in local minimums and having low search efficiency, a method of learning Bayesian network structure based on hy- brid binary slap swarm-differential evolution algorithm was proposed. An adaptive scale factor was used to balance local and global search in the swarm grouping stage. The improved mutation operator and crossover operator were taken into salp search strategy and differential search strategy respectively to renew different subswarms in the update stage. Two-point mutation operator was adopted to improve the swarm’s diversity in the stage of merging of subswarms. The convergence analysis of the proposed algorithm demonstrates that best structure can be found through the iterative search of population. Experimental results show that the convergence accuracy and efficiency of the proposed algorithm are im- proved compared with other algorithms.