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　alpha　controls　the　effect　of　herd　behavior　on　consensus.　We　find　that　there　exists　an　optimal　value　of　alpha　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.　(C)　2011　Elsevier　B.V.　All　rights　reserved.