Mining　functional　modules　in　a　protein-protein　interaction　(PPI)　network　contributes　greatly　to　the　understanding　of　biological　mechanism,　thus　how　to　effectively　detect　functional　modules　in　a　PPI　network　has　a　significant　application.　In　this　paper,　we　present　a　hybrid　approach　using　ant　colony　optimization　and　multi-agent　evolution　for　detection　functional　modules　in　PPI　networks.　The　proposed　algorithm　enhances　the　performance　of　ant　colony　optimization　by　incorporating　multi-agent　evolution　for　detecting　functional　modules.　In　the　ant　colony　optimization　process,　a　new　heuristic,　which　merges　topological　characteristics　with　functional　information　function,　is　introduced　to　effectively　conduct　ants　searching　in　finding　optimal　results.　Thereafter,　the　multi-agent　evolutionary　process　based　on　an　energy　function　is　performed　to　move　out　of　local　optima　and　obtain　some　enclosed　connecting　subgraphs　which　represent　functional　modules　mined　in　a　PPI　network.　Finally,　systematic　experiments　have　been　conducted　on　four　benchmark　testing　sets　of　yeast　networks.　Experimental　results　show　that　the　hybrid　approach　is　more　effective　compared　to　several　other　existing　algorithms.　(C)　2013　Elsevier　B.V.　All　rights　reserved.