Simulation-based optimization using simulated annealing with ranking and selection

In this paper, we present a new iterative method that combines the simulate d annealing method and the ranking and selection procedures for solving dis crete stochastic optimization problems. The number of visit to every state by the proposed algorithm is used to estimate the optimal solution. We show that the configuration that has been visited most often in the first m ite rations converges almost surely to a globally optimum solution. We present empirical results that illustrate the performance of the proposed method.