Functional　module　detection　in　protein-protein　interaction　(PPI)　network　is　one　important　content　of　the　proteomics　research　in　the　post-genomic　era.　Nowadays　the　swarm　intelligence　and　evolutionary　based　approaches　have　become　effective　ways　for　detecting　functional　modules.　This　paper　proposes　a　novel　hybrid　approach　of　fireworks　algorithm　and　differential　evolution　strategies　for　functional　module　detection　in　PPI　networks　(called　HFADE-FMD).　HFADE-FMD　first　initializes　each　firework　individual　into　a　candidate　functional　module　partition　based　on　label　propagation　according　to　the　topological　and　functional　information　between　protein　nodes.　Then　HFADE-FMD　uses　the　explosion　operator　of　firework　algorithm,　and　mutation,　crossover　and　selection　strategies　of　differential　evolution　algorithm　to　iteratively　search　for　better　functional　module　partitions.　To　verify　the　performance　of　HFADE-FMD,　this　paper　compared　it　with　ten　competitive　methods　on　four　public　PPI　datasets.　The　experimental　results　show　that　HFADE-FMD　achieves　prominent　performance　with　respective　to　Recall,　Sn,　PPV,　and　ACC　metrics　while　performing　well　in　terms　of　Precision　and　F-measure　metrics.　Thus,　it　is　able　to　more　accurately　detect　functional　modules　and　help　biologists　to　find　some　novel　biological　insights.