Optimizing the ensemble for equilibration in broad-histogram Monte Carlo simulations

An adaptive algorithm which optimize the statistical-mechanical ensemble in a generalized broad-histogram Monte Carlo simulation to maximize the system's rate of round trips in total energy was presented. It was found that the scaling of the mean round-trip time from the ground state to the maximum entropy state for this local-update method was O([NInN])²) for both the ferromagnetic and the fully frustrated two-dimensional Ising model with N spins. It was shown that the algorithm substantially outperforms flat-histogram methods such as the Wang-Landau algorithm. This algorithm was widely applicable to study the equilibrium behavior of complex system, such as glasses, dense fluids or polymers.