Comparative Studies of Metamodeling Techniques Under Multiple Modeling Criteria

Despite advances in computer capacity, the enormous computational cost of running complex engineering simulations makes it impractical to rely exclusively on simulation for the purpose of design optimization. To cut down the cost, surrogate models, also known as metamodels, are constructed from and then used in place of the actual simulation models. In the paper, we systematically compare four popular metamodeling techniques Polynomial Regression, Multivariate Adaptive Regression Splines, Radial Basis Functions, and Kriging based on multiple performance criteria using fourteen test problems representing di erent classes of problems. Our objective in this study is to investigate the advantages and disadvantages these four metamodeling techniques using multiple criteria multiple test problems rather than a single measure of merit and a single test problem.