Methodology for Global Optimization of Computationally Expensive Design Problems

Abstract

The design of unconventional systems requires early use of high-fidelity physics-based tools to search the design space for improved and potentially optimum designs. Current methods for incorporating these computationally expensive tools into early design for the purpose of reducing uncertainty are inadequate due to the limited computational resources that are available in early design. Furthermore, the lack of finite difference derivatives, unknown design space properties, and the possibility of code failures motivates the need for a robust and efficient global optimization (EGO) algorithm. A novel surrogate model-based global optimization algorithm capable of efficiently searching challenging design spaces for improved designs is presented. The algorithm, called fBcEGO for fully Bayesian constrained EGO, constructs a fully Bayesian Gaussian process (GP) model through a set of observations and then uses the model to make new observations in promising areas where improvements are likely to occur. This model remedies the inadequacies of likelihood-based approaches, which may provide an incomplete inference of the underlying function when function evaluations are expensive and therefore scarce. A challenge in the construction of the fully Bayesian GP model is the selection of the prior distribution placed on the model hyperparameters. Previous work employs static priors, which may not capture a sufficient number of interpretations of the data to make any useful inferences about the underlying function. An iterative method that dynamically assigns hyperparameter priors by exploiting the mechanics of Bayesian penalization is presented. fBcEGO is incorporated into a methodology that generates relatively few infeasible designs and provides large reductions in the objective function values of design problems. This new algorithm, upon implementation, was found to solve more nonlinearly constrained algebraic test problems to higher accuracies relative to the global minimum than other popular surrogate model-based global optimization algorithms and obtained the largest reduction in the takeoff gross weight objective function for the case study of a notional 70-passenger regional jet when compared with competing design methods.

Constrained global optimization via a DIRECT-type constraint-handling technique and an adaptive metamodeling strategy

“…To directly handle problems with both expensive objective and constraints, some researchers (Sasena et al 2002;Basudhar et al 2012;Koullias and Mavris 2014) extended the efficient global optimization (EGO) algorithm by using the EI idea to tackle constraints. Kazemi et al (2011) developed the original MPS algorithm for constrained problems by using the constraint-importance idea with penalty method (CiMPS).…”