A Framework for Managing Approximation Models in place of Expensive Simulations in Optimization

최적화에서의 근사모델 관리기법의 활용

In optimization problems, computationally intensive or expensive simulations hinder the use of standard optimization techniques because the computational expense is too heavy to implement them at each iteration of the optimization algorithm. Therefore, those expensive simulations are often replaced with approximation models which can be evaluated nearly free. However, because of the limited accuracy of the approximation models, it is practically impossible to find an exact optimal point of the original problem. Significant efforts have been made to overcome this problem. The approximation models are sequentially updated during the iterative optimization process such that interesting design points are included. The interesting points have a strong influence on making the approximation model capture an overall trend of the original function or improving the accuracy of the approximation in the vicinity of a minimizer. They are successively determined at each iteration by utilizing the predictive ability of the approximation model. This paper will focuses on those approaches and introduces various approximation methods.