Identifying　functional　modules　in　PPI　networks　contributes　greatly　to　the　understanding　of　cellular　functions　and　mechanisms.　Recently,　the　swarm　intelligence-based　approaches　have　become　effective　ways　for　detecting　functional　modules　in　PPI　networks.　This　paper　presents　a　new　computational　approach　based　on　bacterial　foraging　optimization　for　functional　module　detection　in　PPI　networks　(called　BFO-FMD).　In　BFO-FMD,　each　bacterium　represents　a　candidate　module　partition　encoded　as　a　directed　graph,　which　is　first　initialized　by　a　random-walk　behavior　according　to　the　topological　and　functional　information　between　protein　nodes.　Then,　BFO-FMD　utilizes　four　principal　biological　mechanisms,　chemotaxis,　conjugation,　reproduction,　and　elimination　and　dispersal　to　search　for　better　protein　module　partitions.　To　verify　the　performance　of　BFO-FMD,　we　compared　it　with　several　other　typical　methods　on　three　common　yeast　datasets.　The　experimental　results　demonstrate　the　excellent　performances　of　BFO-FMD　in　terms　of　various　evaluation　metrics.　BFO-FMD　achieves　outstanding　Recall,　F-measure,　and　PPV　while　performing　very　well　in　terms　of　other　metrics.　Thus,　it　can　accurately　predict　protein　modules　and　help　biologists　to　find　some　novel　biological　insights.