Bayesian network learning algorithm based on unconstrained optimization and ant colony optimization

Structure learning of Bayesian networks is a well-researched but computationally hard task. For learning Bayesian networks, this paper proposes an improved algorithm based on unconstrained optimization and ant colony optimization (U-ACO-B) to solve the drawbacks of the ant colony optimization (ACO-B). In this algorithm, firstly, an unconstrained optimization problem is solved to obtain an undirected skeleton, and then the ACO algorithm is used to orientate the edges, thus returning the final structure. In the experimental part of the paper, we compare the performance of the proposed algorithm with ACO-B algorithm. The experimental results show that our method is effective and greatly enhance convergence speed than ACO-B algorithm.