Community　structure　detection　in　complex　networks　has　been　intensively　investigated　in　recent　years.　In　this　paper,　we　propose　an　adaptive　approach　based　on　ant　colony　clustering　to　discover　communities　in　a　complex　network.　The　focus　of　the　method　is　the　clustering　process　of　an　ant　colony　in　a　virtual　grid,　where　each　ant　represents　a　node　in　the　complex　network.　During　the　ant　colony　search,　the　method　uses　a　new　fitness　function　to　percept　local　environment　and　employs　a　pheromone　diffusion　model　as　a　global　information　feedback　mechanism　to　realize　information　exchange　among　ants.　A　significant　advantage　of　our　method　is　that　the　locations　in　the　grid　environment　and　the　connections　of　the　complex　network　structure　are　simultaneously　taken　into　account　in　ants　moving.　Experimental　results　on　computer-generated　and　real-world　networks　show　the　capability　of　our　method　to　successfully　detect　community　structures.　(C)　2013　Elsevier　B.V.　All　rights　reserved.