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 |
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页(从-至) | 329-339 |
页数 | 11 |
期刊 | Natural Computing |
卷 | 22 |
期 | 2 |
DOI | |
出版状态 | 已出版 - 6月 2023 |