Adaptive extensions of the Nelder and Mead Simplex Method for optimization of stochastic simulation models
AbstractWe consider the Nelder and Mead Simplex Method for the optimization of stochastic simulation models. Existing and new adaptive extensions of the Nelder and Mead simplex method designed to improve the accuracy and consistency of the observed best point are studied. We comparethe performance of the extensions on a small microsimulation model, as well as on five test functions. We found that gradually decreasing the noise during an optimization run is the most preferred approach for stochastic objective functions. The amount of computation effort needed for successful optimization is very sensitive to the timing of noise reduction and to the rate of decrease of the noise. Restarting the algorithm during the optimization run, in the sense that the algorithm applies a fresh simplex at certain iterations during an optimization run, has adverse effects in our tests for the microsimulation model and for most test functions.
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Bibliographic InfoPaper provided by Erasmus University Rotterdam, Econometric Institute in its series Econometric Institute Report with number EI 2000-22/A.
Date of creation: 25 May 2000
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simulation; health care; programming; Nelder and Mead Simplex Method;
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- Tan, S.Y.G.L. & Oortmarssen, G.J. van & Piersma, N., 2000. "Estimting parameters of a microsimulation model for breast cancer screening using the score function method," Econometric Institute Report EI 2000-35/A, Erasmus University Rotterdam, Econometric Institute.
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