  We present an adaptive algorithm which optimizes the statistical-mechanical
ensemble in a generalized broad-histogram Monte Carlo simulation to maximize
the system's rate of round trips in total energy. The scaling of the mean
round-trip time from the ground state to the maximum entropy state for this
local-update method is found to be O([N log N]^2) for both the ferromagnetic
and the fully frustrated 2D Ising model with N spins. Our new algorithm thereby
substantially outperforms flat-histogram methods such as the Wang-Landau
algorithm.
