To　solve　the　problem　that　the　swarm　intelligence　clustering　methods　are　time-consuming　in　detecting　functional　modules　in　protein-protein　interaction　networks,　this　paper　proposes　a　method　based　on　fireworks　algorithm　for　functional　module　detection　in　protein-protein　interaction　networks　(FWA-FMD).　First,　each　firework　individual　was　initialized　as　a　candidate　solution　based　on　the　label　propagation　idea　by　combining　the　topological　and　functional　information.　Then　in　each　generation　of　evolution,　each　firework　individual　was　optimized　by　using　explosion　operation　with　local　search　and　global　search　self-adjustment　capabilities,　and　the　next　generation　of　fireworks　individuals　were　selected　by　using　elite　retention　and　roulette　strategy.　Finally,　the　nodes　with　the　same　label　in　the　optimal　firework　were　divided　into　the　same　function　module　to　obtain　the　final　function　module　detection　result.　Functional　module　detection　results　on　the　four　protein-protein　interaction　network　datasets　of　Saccharomyces　cerevisiae　and　Homo　sapiens　were　evaluated　by　using　two　standard　functional　module　datasets　as　benchmarks,　which　shows　that　the　FWA-FMD　algorithm　not　only　costs　less　time　than　GA-PPI,　ACC-FMD,　and　BFO-FMD,　but　also　has　obvious　advantages　in　many　evaluation　indicators　compared　with　some　representative　algorithms,　which　can　better　identify　functional　modules.　©　2019,　Editorial　Board　of　Journal　of　Harbin　Institute　of　Technology.　All　right　reserved.