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 ca lculation of equilibrium thermodynamic properties of spin systems is p resented. The algorithm proposed performs trial moves based on the gen eralized 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 Mo nte Carlo.