We　propose　a　nonlinear　voter　model　to　study　the　emergence　of　global　consensus　in　opinion　dynamics.　In　our　model,　agent　i　agrees　with　one　of　binary　opinions　with　the　probability　that　is　a　power　function　of　the　number　of　agents　holding　this　opinion　among　agent　i　and　its　nearest　neighbors,　where　an　adjustable　parameter　α　controls　the　effect　of　herd　behavior　on　consensus.　We　find　that　there　exists　an　optimal　value　of　α　leading　to　the　fastest　consensus　for　lattices,　random　graphs,　small-world　networks　and　scale-free　networks.　Qualitative　insights　are　obtained　by　examining　the　spatiotemporal　evolution　of　the　opinion　clusters.　©　2011　Elsevier　B.V.　All　rights　reserved.