  We introduce a new Monte Carlo method by incorporating a guided distribution
function to the conventional Monte Carlo method. In this way, the efficiency of
Monte Carlo methods is drastically improved. To further speed up the algorithm,
we include two more ingredients into the algorithm. First, we freeze the
sub-patterns that have high probability of appearance during the search for
optimal solution, resulting in a reduction of the phase space of the problem.
Second, we perform the simulation at a temperature which is within the optimal
temperature range of the optimization search in our algorithm. We use this
algorithm to search for the optimal path of the traveling salesman problem and
the ground state energy of the spin glass model and demonstrate that its
performance is comparable with more elaborate and heuristic methods.
