MOEA/D with gradient-enhanced kriging for expensive multiobjective optimization.
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| Title: | MOEA/D with gradient-enhanced kriging for expensive multiobjective optimization. |
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| Authors: | Liu, Fei, Zhang, Qingfu, Han, Zhonghua |
| Source: | Natural Computing; Jun2023, Vol. 22 Issue 2, p329-339, 11p |
| Subject Terms: | KRIGING, TRANSONIC aerodynamics, PARTIAL differential equations, ENGINEERING design |
| 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. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
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