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.