Bayesian network structure learning by opposition-based learning

As a classical basic model for causal inference, Bayesian networks are of vital importance both in artificial intelligence with uncertainty and interpretability. The significant status of Bayesian networks in these research orientations depends on its topological structure, namely directed acyclic graphs. Bayesian network structure learning is a well-known NP-hard problem, and its computation accuracy is still worth being further studied. In this paper, we propose a new Bayesian network structure learning algorithm, OP-PSO-DE, which combines Particle Swarm Optimization(PSO) and Differential Evolution to search for the optimal structure. Since the computation complexity of BN structure learning increases exponentially with the number of nodes, the proposed algorithm incorporates opposition-based learning to narrow the search space of heuristic algorithms, which can effectively accelerate the searching process. Experimental results show that the proposed algorithm achieves better performances than other state-of-the-art structure learning algorithms when the sample size is 500. The source code of the paper can be found at this link: https://github.com/sunbaodan-hrbeu/paper_code .