Empirical model-building and response surface
Empirical model-building and response surface
An asymptotic allocation for simultaneous simulation experiments
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
Selecting the best system: selecting the best system: theory and methods
Proceedings of the 35th conference on Winter simulation: driving innovation
Extension of the direct optimization algorithm for noisy functions
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
On the geometry phase in model-based algorithms for derivative-free optimization
Optimization Methods & Software
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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In many real-world optimization problems, the objective function may come from a simulation evaluation so that it is (a) subject to various levels of noise, (b) not differentiable, and (c) computationally hard to evaluate. In this paper, we modify Powell's UOBYQA algorithm to handle those real-world simulation problems. Our modifications apply Bayesian techniques to guide appropriate sampling strategies to estimate the objective function. We aim to make the underlying UOBYQA algorithm proceed efficiently while simultaneously controlling the amount of computational effort.