  In this paper we present a dynamical Monte Carlo algorithm which is
applicable to systems satisfying a clustering condition: during the dynamical
evolution the system is mostly trapped in deep local minima (as happens in
glasses, pinning problems etc.). We compare the algorithm to the usual Monte
Carlo algorithm, using as an example the Bernasconi model. In this model, a
straightforward implementation of the algorithm gives an improvement of several
orders of magnitude in computational speed with respect to a recent, already
very efficient, implementation of the algorithm of Bortz, Kalos and Lebowitz.
