Functional　module　identification　in　a　Protein-Protein　Interaction　(PPI)　network　is　one　of　the　most　important　and　challenging　tasks　in　computational　biology.　For　detecting　functional　modules,　it　is　difficult　to　solve　the　problem　directly　and　always　results　in　a　low　accuracy　and　a　large　discard　rate.　In　this　paper,　we　present　a　novel　algorithm　of　ant　colony　optimization　with　multi-agent　evolution　for　detecting　functional　modules.　The　proposed　ACO-MAE　algorithm　enhances　the　performance　of　ant　colony　optimization　(ACO)　by　incorporating　multi-agent　evolution　(MAE).　First,　the　ant　colony　optimization　for　solving　Traveling　Salesman　Problems　(TSP)　is　conducted　to　construct　primary　clustering　results.　Then,　the　multi-agent　evolutionary　process　is　performed　to　move　out　of　local　optima.　From　simulation　results,　it　is　shown　that　the　proposed　ACO-MAE　algorithm　has　superior　performance　when　compared　to　other　existing　algorithms.　©　2012　Springer-Verlag.