MOEA/D with gradient-enhanced kriging for expensive multiobjective optimization

Fei Liu, Qingfu Zhang, Zhonghua Han

科研成果: 期刊稿件文章同行评审

6 引用 (Scopus)

摘要

In many real-world engineering design optimization problems, objective function evaluations are very time costly and often conducted by solving partial differential equations. Gradients of the objective functions can be obtained as a byproduct. Naturally, these problems can be solved more efficiently if gradient information is used. This paper studies how to do expensive multiobjective optimization when gradients are available. We propose a method, called MOEA/D–GEK, which combines MOEA/D and gradient-enhanced kriging. The gradients are used for building kriging models. Experimental studies on a set of test instances and an engineering problem of aerodynamic design optimization for a transonic airfoil show the high efficiency and effectiveness of our proposed method.

源语言英语
页(从-至)329-339
页数11
期刊Natural Computing
22
2
DOI
出版状态已出版 - 6月 2023

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