Differential-Evolution-Based Coevolution Ant Colony Optimization Algorithm for Bayesian Network Structure Learning

Learning the Bayesian networks (BNs) structure from data has received increasing attention. Many heuristic algorithms have been introduced to search for the optimal network that best matches the given training data set. To further improve the performance of ant colony optimization (ACO) in learning the BNs structure, this paper proposes a new improved coevolution ACO (coACO) algorithm, which uses the pheromone information as the cooperative factor and the differential evolution (DE) as the cooperative strategy. Different from the basic ACO, the coACO divides the entire ant colony into various sub-colonies (groups), among which DE operators are adopted to implement the cooperative evolutionary process. Experimental results demonstrate that the proposed coACO outperforms the basic ACO in learning the BN structure in terms of convergence and accuracy