Chapter 11: Optimization Methods Based on Surrogate Models

Yuehua Gao

School of Traffic & Transportation Engineering, Dalian Jiaotong University, Dalian, Liaoning, China

Lih-Sheng Turng

Polymer Engineering Center, Department of Mechanical Engineering, University of Wisconsin–Madison, Madison, Wisconsin, USA

Peng Zhao

State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, Zhejiang, China

Huamin Zhou

State Key Laboratory of Materials Processing and Die & Mould Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China

Considering the limitations of conventional categories, these approaches employ modeling methods to establish a surrogate model (also called a metamodel) based on CAE simulations, and thus substitutes the simulations with the surrogate model in the subsequent optimization procedure. Optimization is usually expensive and time-consuming as it requires either excessive computing time and/or extensive physical experimentation. Methods that can extract useful information from evaluated entries and formulate models to correlate process conditions and objectives provide the maximum benefit.

In this chapter the optimization methods based on surrogate model are discussed. This type of model can reduce the cost of optimization by using a lower fidelity model most of the time, with occasional recourse to the high fidelity model. Unlike other optimization methods, this class optimization method makes the optimization algorithm ...

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