Taboo-based Monte Carlo search as a method to improve sampling efficiency

Taboo-based Monte Carlo search which restricts the sampling of the region n ear an old configuration, is developed. In this procedure, Monte Carlo simu lation and random search method are combined to improve the sampling effici ency. The feasibility of this method is tested on global optimization of a continuous model function, melting of the 256 Lennard-Jones particles at T* = 0.680 and rho* = 0.850 and polypeptides (alanine dipeptide and Metenkeph alin). From the comparison of results for the model function between our me thod and other methods, we find the increase of convergence rate and the hi gh possibility of escaping from the local energy minima. The results of the Lennard-Jones solids and polypeptides show that the convergence property t o reach the equilibrium state is better than that of others. It is also fou nd that no significant bias in ensemble distribution is detected, though ta boo-based Monte Carlo search does not sample the correct ensemble distribut ion owing to the restriction of the sampling of the region near an old conf iguration.