改进细菌觅食优化算法在贝叶斯网络r结构学习中的应用

摘要

针对目前已有的贝叶斯网络结构学习算法一般存在算法易早熟、学习效果不理想、算法效率较低等问题,提出了基于改进细菌觅食优化算法的贝叶斯网络结构学习策略,对传统细菌算法中的趋化算子、繁殖算子和迁移算子进行了改进.将自适应理论应用于细菌游动步长的计算和繁殖个体的选择中;在迁移算子的迁移概率计算中,引入了遗传算法中的轮盘赌方法;在互信息理论的基础上,给出了一种新的网络结构随机进化方法,代替了传统细菌算法中的随机迁移.对不同规模的经典贝叶斯网络进行了仿真实验.研究结果表明,该算法在贝叶斯网络结构学习方面,在收敛性上表现稍逊于别的算法,但在学习效果上,特别是针对结构相对复杂的网络,优势明显.%Aiming at the problems that the existing Bayesian network structure learning algorithms always had indequacies such as easy to premature convergence, low learning efficiency, a Bayesian network structure learning strategy based on improved bacterial foraging optimiza-tion algorithm was proposed, which improved the chemotaxis operator, reproduction operator and elimination operator in the traditional bacte-rial foraging optimization. The adaptive theory was applied to the calculation of bacterial run step size and selection of reproduction individu-als. In the migration probability calculation of the migration operator, the roulette method in the genetic algorithm was introduced. Based on mutual information theory, a new random evolution method of the network structure was created to take place of the random elimination meth-od in the traditional bacterial optimization. Experiments on classical Bayesian networks of different scales were proceeded. The results indi-cate that the algorithm is effective in Bayesian network structure learning, the performance in convergence is inferior to other algorithms, but in the learning effect, especially for the complex structure of the network, the advantages are obvious.