A surrogate-model-based method for constrained optimization

Abstract

This paper describes an algorithm and provides test results for surrogate-model-based optimization. In this type of optimization, the objective and constraint functions are represented by global surrogates, i.e. response models, of the true problem responses. In general, guarantees of global optimality are not possible. However, a robust surrogate-model-based optimization method is presented here that has good global search properties, and proven local convergence results. This paper describes methods for handling three key issues in surrogate-model-based optimization. These issues are maintaining a balance of effort between global design space exploration and local optimizer region refinement, maintaining good surrogate model conditioning as points pile up in local regions, and providing a provably convergent method for ensuring local optimality. Acknowledgments: Work of the first author was supported by NSERC (Natural Sciences and Engineering Research Council) fellowship PDF-2074321998, and the first three authors was supported by DOE DE-FG03-95ER25257, AFOSR F49620-98-10267, The Boeing Company, Sandia LG-4253, ExxonMobil and CRPC CCR-9120008. Copyright ©2000 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.