A Model Reference Adaptive Search Method for Stochastic Global Optimization
نویسندگان
چکیده
We propose a new method called Stochastic Model Reference Adaptive Search (SMRAS) for finding a global optimal solution to a stochastic optimization problem in situations where the objective functions cannot be evaluated exactly, but can be estimated with some noise (or uncertainty), e.g., via simulation. SMRAS is a generalization of the recently proposed Model Reference Adaptive Search (MRAS) method for deterministic global optimization with appropriate adaptations required for stochastic domains. We prove that SMRAS converges asymptotically to a global optimal solution with probability one for both stochastic continuous and discrete (combinatorial) problems. Numerical studies are also carried out to illustrate the method.
منابع مشابه
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 ...
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