As　a　powerful　formalism,　Bayesian　networks　play　an　increasingly　important　role　in　the　Uncertainty　Field.　This　paper　proposes　a　hybrid　method　to　discover　the　knowledge　represented　in　Bayesian　networks.　The　hybrid　method　combines　dependency　analysis,　ant　colony　optimization　(ACO),　and　the　simulated　annealing　strategy.　Firstly,　the　new　method　uses　order-0　independence　tests　with　a　self-adjusting　threshold　value　to　reduce　the　size　of　the　search　space,　so　that　the　search　process　takes　less　time　to　find　the　near-optimal　solution.　Secondly,　better　Bayesian　network　models　are　generated　by　using　an　improved　ACO　algorithm,　where　a　new　heuristic　function　is　introduced　to　further　enhance　the　search　effectiveness　and　efficiency.　Finally,　an　optimization　scheme　based　on　simulated　annealing　is　employed　to　improve　the　optimization　efficiency　in　the　stochastic　search　process　of　ants.　In　a　number　of　experiments　and　comparisons,　the　hybrid　method　outperforms　the　original　ACO-B　which　uses　ACO　and　some　other　network　learning　algorithms.　(C)　2011　Elsevier　B.V.　All　rights　reserved.