Department of Systems Engineering & Operation Research

We propose a randomized search method called Stochastic Model Reference Adaptive Search (SMRAS) for solving stochastic optimization problems in situations where the objective functions cannot be evaluated exactly, but can be estimated with some noise (or uncertainty), e.g., via simulation. The method is a generalization of the recently proposed Model Reference Adaptive Search (MRAS) method for deterministic global optimization, and is based on sampling from an underlying probability distribution “model” on the solution space, which is updated iteratively after evaluating the performance of the samples at each iteration. We show global convergence of SMRAS for both stochastic continuous and discrete (combinatorial) problems, and carry out numerical studies to illustrate the performance of the method.