A response-modeling approach to global optimization and optimization under uncertainty

The general problem considered is an optimization problem involving selecting design parameters that yield an optimal response. We assume some initial response data are available and further experimentation (physical experiments and/or computer simulations) are to be used to obtain more information. We assume further that resources and system complexity together restrict the number of experiments or simulations that can be performed. Consequently, levels of the design parameters used in the simulations must be selected in a way that will efficiently approximate the optimal design ‘location’ and the optimal value. This paper describes an algorithmic ‘response-modeling’ approach for performing this selection. The algorithm is demonstrated using a simple analytical surface and is applied to two additional problems that have been addressed in the literature for comparison with other approaches.