In　recent　years,　the　detection　of　network　community　structures　has　been　adopted　to　address　the　distributed　resource　optimization　issue　in　distributed　networks.　This　paper　presents　an　autonomy-oriented　distributed　search　strategy　to　tackle　it.　The　strategy　is　based　on　the　ideas　of　self-organization　and　positive　feedback　from　the　methodology　of　Autonomy-Oriented　Computing　(AOC).　The　strategy　uses　bio-inspired　autonomous　agents　which　can　use　their　edges　to　distinguish　the　network　communities.　Agents　are　equipped　with　one　behavior　(move)　and　three　selections　(best　selection,　better　selection　and　random　selection).　At　every　moment,　agents　probabilistically　choose　a　behavior　to　perform.　Experimental　results　indicate　that　the　strategy　has　a　positive　influence　on　system　performance.