基于蚁群优化的贝叶斯网络学习

针对贝叶斯网络学习中的混合算法容易缩小搜索空间，同时易陷入局部最优等缺点，提出了基于蚁群优化的贝叶斯网络学习算法。首先应用最大最小父子节点集合算法(max min parents and children, MMPC)来构建无向网络的框架，然后利用蚁群优化算法进行评分〖CD*2〗搜索，通过平衡“开发”和“探索”力度来修补搜索空间并确定网络结构中边的方向。最后应用本算法学习逻辑报警还原机理网（a logical alarm reduction mechanism, ALARM)，结果显示本算法减少了丢失边的数量，得到了更接近真实结构的贝叶斯网络。

Bayesian network learning on algorithm based on ant colony optimization

Accordering to the hybrid Bayesian networks learning algorithms which are easy to narrow the search space and fall into local optimum, a Bayesian network learning algorithm based on ant colony optimization is proposed. Firstly, this paper applies max min parents and children (MMPC) to construct the framework of the undirected network, and then uses ant colony optimization to score search, by balancing the “exploitation” and “exploration” to repair the search space and determine the direction of edges in the network. Finally applying the algorithm to learn a logical alarm reduction mechanism (ALARM) network shows that it reduces the number of missing edges, and gets closer to the real structure of Bayesian network.