Abstract:

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.