基于混合改进鸟群算法的贝叶斯网络结构学习

针对贝叶斯网络结构学习中寻优效率低下、易陷入局部最优的缺陷,提出了一种基于混合改进鸟群算法的贝叶斯网络结构学习算法.首先,通过互信息约束算法迭代初始网络;其次,改进鸟群算法,在经典鸟群算法中加入自适应惯性权重,随着迭代次数的增加动态调整搜索空间、改变收敛速度;最后,将改进的鸟群算法作为搜索策略,进行贝叶斯网络结构寻优.实验结果表明:改进的算法在寻优过程中不仅有较好的准确率和较快的收敛速度,而且具有良好的全局寻优能力.

Bayesian Network Structure Learning Based on Hybrid Improved Bird Swarm Algorithm

Aimed at the problems that efficiency is low in optimization and there are defects easy to fall into local optimum in Bayesian network structure learning, a new Bayesian network structure learning algorithm based on hybrid improved Bird swarm algorithm is proposed. Firstly, the initial network is constrained by Mutual information. Secondly, the Bird swarm algorithm is improved by adding adaptive inertia weight. With the increase of the number of iterations, the adaptive inertia weight is adjusted to dynamically adjust the search space of algorithm and change the convergence speed. Finally, taking the improved Bird swarm algorithm as a search strategy, optimization is given to the structure of Bayesian network. The experimental results show that the proposed algorithm is not only good in accuracy and fast at convergence speed, but also good in global optimization ability.