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A Multi-Fidelity Surrogate-Model-Assisted Evolutionary Algorithm for Computationally Expensive Optimization Problems
Abstrakt
Integrating data-driven surrogate models and simulation models of different accuracies (or fideli-ties) in a single algorithm to address computationally expensive global optimization problems has recently attracted considerable attention. However, handling discrepancies between simulation models with multiple fidelities in global optimization is a major challenge. To address it, the two major contributions of this paper include: (1) development of a new multi-fidelity surrogate-model-based optimization framework, which substantially improves reliability and efficiency of optimiza-tion compared to many existing methods, and (2) development of a data mining method to address the discrepancy between the low- and high-fidelity simulation models. A new efficient global optimization method is then proposed, referred to as multi-fidelity Gaussian process and radial basis function-model-assisted memetic differential evolution. Its advantages are verified by mathematical benchmark problems and a real-world antenna design automation problem.
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Autorzy (3)
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Bo Liu
- Glyndwr University Department of Computing
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Qingfu Zhang
- University of Essex School of Computer Science & Electronic Engineering
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Informacje szczegółowe
- Kategoria:
- Publikacja w czasopiśmie
- Typ:
- artykuł w czasopiśmie wyróżnionym w JCR
- Opublikowano w:
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Journal of Computational Science
nr 12,
strony 28 - 37,
ISSN: 1877-7503 - Język:
- angielski
- Rok wydania:
- 2016
- Opis bibliograficzny:
- Liu B., Kozieł S., Zhang Q.: A Multi-Fidelity Surrogate-Model-Assisted Evolutionary Algorithm for Computationally Expensive Optimization Problems// Journal of Computational Science. -Vol. 12, (2016), s.28-37
- DOI:
- Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1016/j.jocs.2015.11.004
- Weryfikacja:
- Politechnika Gdańska
wyświetlono 144 razy
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