ADAPTIVE RESPONSE SURFACE METHOD - A GLOBAL OPTIMIZATION SCHEME FOR APPROXIMATION-BASED DESIGN PROBLEMS

TLDR

Design problems with computation-intensive analysis or simulation typically use approximation models to reduce computational cost, and most methods iteratively refine these models by adjusting variable limits. This study introduces the adaptive response surface method (ARSM) to avoid local optima and locate the global design optimum with a modest number of objective function evaluations. ARSM constructs quadratic surrogate models of the objective function within a gradually reduced design space to guide the search. Extensive benchmark and industrial tests show that ARSM reliably identifies global optima with few evaluations, while its advantages and limitations are discussed.

Abstract

Abstract For design problems involving computation-intensive analysis or simulation processes, approximation models are usually introduced to reduce computation lime. Most approximation-based optimization methods make step-by-step improvements to the approximation model by adjusting the limits of the design variables. In this work, a new approximation-based optimization method for computation-intensive design problems - the adaptive response surface method(ARSM), is presented. The ARSM creates quadratic approximation models for the computation-intensive design objective function in a gradually reduced design space. The ARSM was designed to avoid being trapped by local optima and to identify the global design optimum with a modest number of objective function evaluations. Extensive tests on the ARSM as a global optimization scheme using benchmark problems, as well as an industrial design application of the method, are presented. Advantages and limitations of the approach are also discussed