  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.
