A Global Optimization Algorithm Based on C1 Piecewise Response Surface Patches

Abstract

The surrogate objective function is constructed using C 1 piecewise response surface based on the design sensitivity analysis using FEM for the global optimization. The sampling points used for constructing the response surface are adaptively inserted during the process. Applications to benchmark problems and the optimum design of a permanent magnet system show the effectiveness of the proposed algorithm.

Robust optimal design of low frequency metamaterials considering the fabrication imperfections and parameter fluctuations

“…Because a number of solutions will be sampled in the neighbor of an individual to evaluate its robustness, the computational burden for a robust optimization is extremely higher than that for a global optimization. To address this issue, a surrogate model is generally used in lieu of the heavy simulation model (Yao et al , 2006; An et al , 2018; Ho and Yang, 2012; Ong et al , 2006; Silva et al , 2018; Tenne and Armfield, 2008). The polynomial chaos expansion is used as a stochastic response surface model of the objective function for efficient robustness performance computations, and the numerical results on two case studies confirm the feasibility (Ho and Yang, 2012).…”