An Improved Particle Swarm Optimization Algorithm for Bayesian Network Structure Learning via Local Information Constraint

At present, in the application of Bayesian network (BN) structure learning algorithm for structure learning, the network scale increases with the increase of number of nodes, resulting in a large scale of structure search space, which is difficult to calculate, and the existing learning algorithms are inefficient, making BN structure learning difficulty increase. To solve this problem, a BN structure optimization method based on local information is proposed. Firstly, it proposes to construct an initial network framework with local information and uses the Max-Min Parents and Children (MMPC) algorithm to construct an undirected network framework to reduce the search space. Then the particle swarm optimization (PSO) algorithm is used to strengthen the algorithm's optimization ability by constructing a new position and velocity update rule and improve the efficiency of the algorithm. Experimental results show that under the same sample data set, the algorithm can obtain a more accurate BN structure while converging quickly, which verifies the correctness and effectiveness of the algorithm.