Multi-objective genetic algorithms, which require a large number of fitness evaluations before obtaining a Pareto set, can become computationally intractable when applied to practical engineering design problems involving computationally expensive simulations. In this work, an on-line multi-fidelity metamodel (MFM) assisted multi-objective genetic algorithm (OLMFM-MOGA) approach is proposed, in which the MFM that can integrate information from both low-fidelity (LF) and high-fidelity (HF) models is constructed to replace the fitness evaluations during the optimization process. Two model management strategies, an individual-based updating strategy considering the interpolation uncertainty from MFM and a generation-based updating strategy considering the discrete degree of the populations, are incorporated in the OLMFM-MOGA. Three numerical examples and an engineering case with different degrees of complexity are used to demonstrate the effectiveness of the proposed approach. Results show that the proposed OLMFM-MOGA is able to obtain similar convergence and diversity of the Pareto frontier to the ones obtained by MOGA with only HF information, while at the same time significantly reducing the number of evaluations of the expensive HF model.
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ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
August 6–9, 2017
Cleveland, Ohio, USA
Conference Sponsors:
- Design Engineering Division
- Computers and Information in Engineering Division
ISBN:
978-0-7918-5813-4
PROCEEDINGS PAPER
An On-Line Multi-Fidelity Metamodel Assisted Multi-Objective Genetic Algorithm
Qi Zhou,
Qi Zhou
Huazhong University of Science & Technology, Wuhan, China
Georgia Institute of Technology, Atlanta, GA
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Yan Wang,
Yan Wang
Georgia Institute of Technology, Atlanta, GA
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Seung-Kyum Choi,
Seung-Kyum Choi
Georgia Institute of Technology, Atlanta, GA
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Ping Jiang
Ping Jiang
Huazhong University of Science & Technology, Wuhan, China
Search for other works by this author on:
Qi Zhou
Huazhong University of Science & Technology, Wuhan, China
Georgia Institute of Technology, Atlanta, GA
Yan Wang
Georgia Institute of Technology, Atlanta, GA
Seung-Kyum Choi
Georgia Institute of Technology, Atlanta, GA
Ping Jiang
Huazhong University of Science & Technology, Wuhan, China
Paper No:
DETC2017-67813, V02BT03A037; 10 pages
Published Online:
November 3, 2017
Citation
Zhou, Q, Wang, Y, Choi, S, & Jiang, P. "An On-Line Multi-Fidelity Metamodel Assisted Multi-Objective Genetic Algorithm." Proceedings of the ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 2B: 43rd Design Automation Conference. Cleveland, Ohio, USA. August 6–9, 2017. V02BT03A037. ASME. https://doi.org/10.1115/DETC2017-67813
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