A　Bayesian　network　(BN)　is　an　important　probabilistic　model　in　the　field　of　artificial　intelligence　and　a　powerful　formalism　used　to　describe　uncertainty　in　the　real　world.　As　science　and　technology　develop,　considerable　data　on　complex　systems　have　been　acquired　by　various　means,　which　presents　a　significant　challenge　regarding　how　to　accurately　and　robustly　learn　a　network　structure　for　a　complex　system.　To　address　this　challenge,　many　BN　structure　learning　methods　based　on　swarm　intelligence　have　been　developed.　In　this　study,　we　perform　a　systematic　comparison　of　three　typical　methods　based　on　ant　colony　optimization,　artificial　bee　colony　algorithm,　and　bacterial　foraging　optimization.　First,　we　analyze　and　summarize　their　main　characteristics　from　the　perspective　of　stochastic　searching.　Second,　we　conduct　thorough　experimental　comparisons　to　examine　the　roles　of　different　mechanisms　in　each　method　by　means　of　multiaspect　metrics,　i.e.,　the　K2　score,　structural　differences,　and　execution　time.　Next,　we　perform　further　experiments　to　validate　the　robustness　of　different　algorithms　on　some　benchmark　data　sets　with　noise.　Finally,　we　present　the　prospects　and　references　for　researchers　who　are　engaged　in　learning　BN　networks.