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Bayesian networks, revered for their adeptness in modeling uncertainty and predicting outcomes, encounter a formidable hurdle during the structure learning phase – an NP-hard problem, posing insurmountable computational challenges for large networks. To surmount this barrier and advance the field, we propose an innovative optimization of the K2PC algorithm for Bayesian network structure learning. Derived from the popular K2 algorithm, our novel optimization ingeniously tackles K2PC's vulnerability to predetermined node order. Leveraging the power of a particle swarm optimization algorithm, we adeptly seek the optimal node ordering, yielding exceptional results. Through rigorous evaluations on benchmark networks, our proposed method surpasses prior approaches in structure difference and accuracy, affirming its potential as a promising avenue for Bayesian network structure learning in large, complex networks. We posit that our novel approach constitutes an important advance in the field of Bayesian network structure learning, with the potential to stimulate additional progress through further scientific investigation.

PSO-K2PC: Bayesian structure learning using optimized K2 algorithm for parents-children detection