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13-08-2022

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

Authors: Fei Liu, Qingfu Zhang, Zhonghua Han

Published in: Natural Computing | Issue 2/2023

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Abstract

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.

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Title: MOEA/D with gradient-enhanced kriging for expensive multiobjective optimization
Authors: Fei Liu
Qingfu Zhang
Zhonghua Han
Publication date: 13-08-2022
Publisher: Springer Netherlands
Published in: Natural Computing / Issue 2/2023
Print ISSN: 1567-7818
Electronic ISSN: 1572-9796
DOI: https://doi.org/10.1007/s11047-022-09907-0

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