Model-Assisted Pattern Search Methods for Optimizing Expensive Computer Simulations
Christopher Siefert, Michael W. Trosset · 2002
The design and analysis of computer experiments (DACE) usually envisions performing a single experiment, then replacing the expensive simulation with an approximation. When the simulation is a nonlinear function to be optimized, DACE may be inefficient, and sequential strategies that synthesize ideas from DACE and numerical optimization may be warranted. We consider several such strategies within a unified framework in which sequential approximations constructed by kriging are used to accelerate a conventional direct search method. Computational experiments reveal that hybrid strategies outperform both DACE and traditional pattern search. 1