Learning the Bayesian networks structure based on ant colony optimization and differential evolution

Learning the structure of Bayesian networks (BNs) has received increasing attention. Based on score+search methods, many heuristic algorithms have been introduced to search the optimal network with the maximum score metric. To overcome the drawback of ant colony optimization (ACO) in solving the BN structure learning, this paper introduces a new algorithm for learning BNs based on the hybrid ACO and differential evolution (DE). In the proposed hybrid algorithm, the entire ant colony is divided into different groups, among which DE operators are adopted to lead the evolutionary process. Differ from the widely used methodologies that combine ACO with constraint-based techniques, our work mainly focuses on improving the inherent search capability of ACO. Experimental results show that the hybrid algorithm outperforms the basic ACO in learning BN structure in terms of convergence and accuracy.