A comparative study of boundary-based intelligent sampling approaches for nonlinear optimization

In order to increase the efficiency and accuracy of design optimization, many efforts have been made on studying the metamodel techniques for effectively representing expensive and complex models. The performance of metamodel-based optimization is largely determined by the sampling method. Mainly due to the difficulty of knowing the appropriate sampling size a priori, termed as intelligent or sequential sampling has gained popularity in recent years. In this paper, a new kind of sampling mode, boundary-based strategies are studied. The major characteristic of intelligent methods is that the new sample is generated based on boundary and other specified samples. In order to evaluate the efficiency and accuracy of such kinds of strategies, a comparative study is implemented with mathematical nonlinear test functions. To validate the feasibility of boundary-based sampling methods, the proposed sampling strategy-based metamodeling technique is used to optimize real-world problems. Comparison of model predications and optimization data shows good agreement.