Algorithms　inspired　by　swarm　intelligence　have　been　used　for　many　optimization　problems　and　their　effectiveness　has　been　proven　in　many　fields.　We　propose　a　new　swarm　intelligence　algorithm　for　structural　learning　of　Bayesian　networks,　BFO-B,　based　on　bacterial　foraging　optimization.　In　the　BFO-B　algorithm,　each　bacterium　corresponds　to　a　candidate　solution　that　represents　a　Bayesian　network　structure,　and　the　algorithm　operates　under　three　principal　mechanisms:　chemotaxis,　reproduction,　and　elimination　and　dispersal.　The　chemotaxis　mechanism　uses　four　operators　to　randomly　and　greedily　optimize　each　solution　in　a　bacterial　population,　then　the　reproduction　mechanism　simulates　survival　of　the　fittest　to　exploit　superior　solutions　and　speed　convergence　of　the　optimization.　Finally,　an　elimination　and　dispersal　mechanism　controls　the　exploration　processes　and　jumps　out　of　a　local　optima　with　a　certain　probability.　We　tested　the　individual　contributions　of　four　algorithm　operators　and　compared　with　two　state　of　the　art　swarm　intelligence　based　algorithms　and　seven　other　well-known　algorithms　on　many　benchmark　networks.　The　experimental　results　verify　that　the　proposed　BFO-B　algorithm　is　a　viable　alternative　to　learn　the　structures　of　Bayesian　networks,　and　is　also　highly　competitive　compared　to　state　of　the　art　algorithms.　©　2015　The　Authors.