The　bat　algorithm　was　used　to　detect　the　functional　modules　in　protein-protein　interaction　networks　(PPINs),　in　order　to　get　better　protein　functional　modules　and　reveal　the　function　of　proteins.　The　position　of　each　bat　individual　represents　a　candidate　functional　module　partition.　Each　protein　node　in　PPIN　and　all　its　neighbor　nodes　form　an　ordered　adjacency　list　and　the　population　is　initialized　by　random　walk　coding　method　in　the　ordered　adjacency　list.　Four　kinds　of　optimization　mechanisms,　namely　directional　local　disturbance,　random　disturbance,　adaptive　variation　based　on　distance　and　frequency,　natural　selection,　are　designed　for　the　random　optimization　of　solutions　in　the　process　of　population　optimization.　The　comparison　experiments　of　the　proposed　algorithm　and　six　classical　algorithms　were　conducted　on　five　yeast　PPIN　datasets　having　different　scales.　Results　showed　that　many　functional　modules　detected　by　the　proposed　method　matched　the　standard　modules　and　the　evaluation　indexes　including　coverage,　recall,　sensitivity,　positive　predictive　value　and　accuracy　were　outstanding,　which　verified　the　validity　of　the　proposed　method.　©　2019,　Zhejiang　University　Press.　All　right　reserved.