A survey of surrogate-assisted evolutionary algorithms for expensive optimization-Reference-Cited by-同舟云学术

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A survey of surrogate-assisted evolutionary algorithms for expensive optimization

Published:2024-08-13 Issue: Volume: Page:
ISSN:2523-8906
Container-title:Journal of Membrane Computing
language:en
Short-container-title:J Membr Comput

Author:

Liang Jing,Lou Yahang,Yu Mingyuan,Bi Ying,Yu Kunjie

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Key Laboratory of Big Data Intelligent Computing, Chongqing University of Posts and Telecommunications

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s41965-024-00165-w.pdf

Reference150 articles.

1. Forrester, A. I., & Keane, A. J. (2009). Recent advances in surrogate-based optimization. Progress in Aerospace Sciences, 45(1–3), 50–79.

2. Le Guennec, Y., Brunet, J.-P., Daim, F.-Z., Chau, M., & Tourbier, Y. (2018). A parametric and non-intrusive reduced order model of car crash simulation. Computer Methods in Applied Mechanics and Engineering, 338, 186–207.

3. Akhtar, T., & Shoemaker, C. A. (2016). Multi objective optimization of computationally expensive multi-modal functions with rbf surrogates and multi-rule selection. Journal of Global Optimization, 64, 17–32.

4. Cai, X., Qiu, H., Gao, L., Yang, P., & Shao, X. (2016). An enhanced RBF-HDMR integrated with an adaptive sampling method for approximating high dimensional problems in engineering design. Structural and Multidisciplinary Optimization, 53, 1209–1229.

5. Queipo, N. V., Haftka, R. T., Shyy, W., Goel, T., Vaidyanathan, R., & Tucker, P. K. (2005). Surrogate-based analysis and optimization. Progress in Aerospace Sciences, 41(1), 1–28.

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