A framework for managing models in nonlinear optimization of computationally expensive functions
David Serafini, John E. Dennis · 1999
A Framework for Managing Models in Nonlinear Optimization of Computationally Expensive Functions by David B. Serafini One of the most significant problems in the application of standard optimization methods to real-world engineering design problems is that the computation of the objective function often takes so much computer time (sometimes hours) that traditional optimization techniques are not practical. A solution that has long been used in this situation has been to approximate the objective function with something much cheaper to compute, called a "model" (or surrogate), and optimize the model instead of the actual objective function. This simple approach succeeds some of the time, but sometimes it fails because there is not sufficient a priori knowledge to build an adequate model. One way to address this problem is to build the model with whatever a priori knowledge is available, and during the optimization process sample the true objective at selected points and use the results...