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.　©　2018　by　the　authors.