@inproceedings{4ee255c2c215418a8b682b4569a1aa0e,
title = "MOEA/D with Gradient-Enhanced Kriging for Expensive Multiobjective Optimization",
abstract = "Expensive multiobjective optimization problem poses a big challenge. In many real-world engineering design problems, the time-consumed function evaluation is done by solving partial differential equations. The partial derivatives of a candidate solution can be calculated as a byproduct. Naturally, these problems can be solved more efficiently if gradient information is used. This paper proposes such a method, called MOEA/D-GEK, which combines MOEA/D and gradient-enhanced Kriging to solve expensive multiobjective problem. The gradient information is used for the construction of the Kriging model. Experimental studies on a set of test instances and a real-world aerodynamic design problem show high efficiency and effectiveness of our proposed method.",
keywords = "Expensive optimization, Gradient-enhanced Kriging, Multiobjective optimization, Pareto optimality, Surrogate model",
author = "Fei Liu and Qingfu Zhang and Zhonghua Han",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021 ; Conference date: 28-03-2021 Through 31-03-2021",
year = "2021",
doi = "10.1007/978-3-030-72062-9\_43",
language = "英语",
isbn = "9783030720612",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "543--554",
editor = "Hisao Ishibuchi and Qingfu Zhang and Ran Cheng and Ke Li and Hui Li and Handing Wang and Aimin Zhou",
booktitle = "Evolutionary Multi-Criterion Optimization - 11th International Conference, EMO 2021, Proceedings",
}