Transfer stacking from low-to high-fidelity: A surrogate-assisted bi-fidelity evolutionary algorithm
Wang H, Jin Y, Yang C, Jiao L (2020)
Applied Soft Computing 92: 106276.
Zeitschriftenaufsatz
| Veröffentlicht | Englisch
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Autor*in
Wang, Handing;
Jin, YaochuUniBi
;
Yang, Cuie;
Jiao, Licheng
![](https://pub.uni-bielefeld.de/images/icon_orcid.png)
Abstract / Bemerkung
Optimization of many real-world optimization problems relies on numerical simulations for function evaluations. In some cases, both high- and low-fidelity simulations are available, where the high fidelity evaluation is accurate but time-consuming, whereas the low-fidelity evaluation is less accurate but computationally cheap. To find an acceptable optimum within a limited budget, it is economical for evolutionary algorithms to use both high- and low-fidelity evaluations in a single optimization search. This paper proposes a novel surrogate-assisted evolutionary algorithm using the transfer stacking technique for bi-fidelity optimization. To this end, a radial basis function network is firstly built to approximate the high-fidelity fitness function as additional low-fidelity evaluation, then a surrogate model transferring the original and additional low-fidelity evaluations to the expensive high-fidelity evaluation is adapted to guide the search. The simulation results on a series of bi-fidelity optimization benchmark problems with resolution, stochastic, and instability errors and a beneficiation processes optimization problem show that the proposed algorithm is both effective and efficient for solving bi-fidelity optimization problems, when their low-fidelity evaluations have resolution and stochastic errors.
Erscheinungsjahr
2020
Zeitschriftentitel
Applied Soft Computing
Band
92
Art.-Nr.
106276
ISSN
1568-4946
Page URI
https://pub.uni-bielefeld.de/record/2978399
Zitieren
Wang H, Jin Y, Yang C, Jiao L. Transfer stacking from low-to high-fidelity: A surrogate-assisted bi-fidelity evolutionary algorithm. Applied Soft Computing. 2020;92: 106276.
Wang, H., Jin, Y., Yang, C., & Jiao, L. (2020). Transfer stacking from low-to high-fidelity: A surrogate-assisted bi-fidelity evolutionary algorithm. Applied Soft Computing, 92, 106276. https://doi.org/10.1016/j.asoc.2020.106276
Wang, Handing, Jin, Yaochu, Yang, Cuie, and Jiao, Licheng. 2020. “Transfer stacking from low-to high-fidelity: A surrogate-assisted bi-fidelity evolutionary algorithm”. Applied Soft Computing 92: 106276.
Wang, H., Jin, Y., Yang, C., and Jiao, L. (2020). Transfer stacking from low-to high-fidelity: A surrogate-assisted bi-fidelity evolutionary algorithm. Applied Soft Computing 92:106276.
Wang, H., et al., 2020. Transfer stacking from low-to high-fidelity: A surrogate-assisted bi-fidelity evolutionary algorithm. Applied Soft Computing, 92: 106276.
H. Wang, et al., “Transfer stacking from low-to high-fidelity: A surrogate-assisted bi-fidelity evolutionary algorithm”, Applied Soft Computing, vol. 92, 2020, : 106276.
Wang, H., Jin, Y., Yang, C., Jiao, L.: Transfer stacking from low-to high-fidelity: A surrogate-assisted bi-fidelity evolutionary algorithm. Applied Soft Computing. 92, : 106276 (2020).
Wang, Handing, Jin, Yaochu, Yang, Cuie, and Jiao, Licheng. “Transfer stacking from low-to high-fidelity: A surrogate-assisted bi-fidelity evolutionary algorithm”. Applied Soft Computing 92 (2020): 106276.
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