A hybrid teaching–learning slime mould algorithm for global optimization and reliability-based design optimization problems-Reference-Cited by-同舟云学术

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A hybrid teaching–learning slime mould algorithm for global optimization and reliability-based design optimization problems

Published:2022-06-03 Issue:19 Volume:34 Page:16617-16642
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Zhong Changting,Li Gang^ORCID,Meng Zeng

Funder

National Basic Research Program of China

National Natural Science Foundation of China

State Key Laboratory of Structural Analysis for Industrial Equipment

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

https://link.springer.com/content/pdf/10.1007/s00521-022-07277-3.pdf

Reference97 articles.

1. Krishna AB, Saxena S, Kamboj VK (2021) A novel statistical approach to numerical and multidisciplinary design optimization problems using pattern search inspired Harris hawks optimizer. Neural Comput Appl 33:7031–7072. https://doi.org/10.1007/s00521-020-05475-5

2. Li G, Hu H (2014) Risk design optimization using many-objective evolutionary algorithm with application to performance-based wind engineering of tall buildings. Struct Saf 48:1–14. https://doi.org/10.1016/j.strusafe.2014.01.002

3. He W, Zeng Y, Li G (2020) An adaptive polynomial chaos expansion for high-dimensional reliability analysis. Struct Multidiscip Optim 62:2051–2067. https://doi.org/10.1007/s00158-020-02594-4

4. Meng Z, Pang Y, Zhou H (2021) An augmented weighted simulation method for high-dimensional reliability analysis. Struct Saf 93:102117. https://doi.org/10.1016/j.strusafe.2021.102117

5. Nezhad HB, Miri M, Ghasemi MR (2019) New neural network-based response surface method for reliability analysis of structures. Neural Comput Appl 31:777–791. https://doi.org/10.1016/j.swevo.2021.100858

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