Multiple search operators selection by adaptive probability allocation for fast convergent multitask optimization-Reference-Cited by-同舟云学术

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Multiple search operators selection by adaptive probability allocation for fast convergent multitask optimization

Published:2024-04-09 Issue:11 Volume:80 Page:16046-16092
ISSN:0920-8542
Container-title:The Journal of Supercomputing
language:en
Short-container-title:J Supercomput

Author:

Wang Zhaoqi,Wang Lei,Jiang Qiaoyong,Duan Xinhui,Wang Zhennan,Wang Liangliang

Funder

National Natural Science Foundation of China

National Social Science Foundation of China

National Education Science Foundation of China

Key Project of Shanxi Provincial Natural Science Basic Research Program

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11227-024-06016-w.pdf

Reference46 articles.

1. Zhang F, Mei Y, Nguyen S, Zhang M, Tan KC (2021) Surrogate-assisted evolutionary multitask genetic programming for dynamic flexible job shop scheduling. IEEE Trans Evolut Comput 25(4):651–665

2. Huang Y, Feng L, Li M, Wang Y, Zhu Z, Tan KC (2023) Fast vehicle routing via knowledge transfer in a reproducing kernel Hilbert space. IEEE Trans Syst Man Cybern: Syst. https://doi.org/10.1109/TSMC.2023.3270308

3. Yang J-Q, Du K-J, Chen C-H, Wang H, Zhang J, Zhan Z-H (2023) Evolutionary multitasking bi-directional particle swarm optimization for high-dimensional feature selection. In: 2023 IEEE Congress on Evolutionary Computation (CEC), IEEE, pp 1–8

4. Feng L, Shang Q, Hou Y, Tan KC, Ong Y-S (2022) Multispace evolutionary search for large-scale optimization with applications to recommender systems. IEEE Trans Artif Intel 4(1):107–120

5. Zhou L, Feng L, Tan KC, Zhong J, Zhu Z, Liu K, Chen C (2020) Toward adaptive knowledge transfer in multifactorial evolutionary computation. IEEE Trans Cybern 51(5):2563–2576

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