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This paper introduces an adaptive Radial Basis Function (RBF) method using weighted improvement for the global optimization of black-box problems subject to box constraints. The proposed method applies rank-one update to efficiently build RBF models and derives a closed form for the leave-one-out cross validation (LOOCV) error of RBF models, allowing an adaptive choice of radial basis functions. In...
Simulation is widely used to evaluate the performance and optimize the design of a complex system. In the past few decades, a great deal of research has been devoted to solving simulation optimization problems, perhaps owing to their generality. However, although there are many problems of practical interests that can be cast in the framework of simulation optimization, it is often difficult to obtain...
This work proposes a new method for approximating the Pareto front of a multi-objective simulation optimization problem (MOP) where the explicit forms of the objective functions are not available. The method iteratively approximates each objective function using a metamodeling scheme and employs a weighted sum method to convert the MOP into a set of single objective optimization problems. The weight...
We assume the existence of a parameterized family of control variates that could be used in a regenerative steady-state simulation. We show how such controls can be generated in the Markov-process setting, discuss the optimization problem of searching for a good choice of parameterization, and develop a strong law and central limit theorem for the resulting estimator.
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