An efficient Monte Carlo algorithm for overcoming broken ergodicity in the simulation of spin systems

A new Monte Carlo algorithm which provides enhanced sampling in the calculation of equilibrium thermodynamic properties of spin systems is presented. The algorithm proposed performs trial moves based on the generalized statistical distributions derived from a modification of the Gibbs-Shannon entropy by Tsallis. Results for a two-dimensional Ising model demonstrate that the algorithm leads to a greatly enhanced rate of barrier crossing and convergence in the calculation of equilibrium thermodynamic averages. Comparison is made with standard Metropolis Monte Carlo.