A many-objective evolutionary algorithm based on three states for solving many-objective optimization problem-Reference-Cited by-同舟云学术

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A many-objective evolutionary algorithm based on three states for solving many-objective optimization problem

Published:2024-08-19 Issue:1 Volume:14 Page:
ISSN:2045-2322
Container-title:Scientific Reports
language:en
Short-container-title:Sci Rep

Author:

Zhao Jiale,Zhang Huijie,Yu Huanhuan,Fei Hansheng,Huang Xiangdang,Yang Qiuling

Funder

National Natural Science Foundation of China

Specific research fund of The Innovation Platform for Academicians of Hainan Province

Key Project of Hainan Province

Publisher

Springer Science and Business Media LLC

Link

https://www.nature.com/articles/s41598-024-70145-8.pdf

Reference40 articles.

1. Lin, H. F. & Tang, C. P. Analysis and optimization of urban public transport lines based on multiobjective adaptive particle swarm optimization. IEEE Trans. Intell. Transp. Syst. 23, 16786–16798. https://doi.org/10.1109/TITS.2021.3086808 (2022).

2. Qin, X., Fang, Z. H. & Zhang, Z. X. Multi-objective optimization for production scheduling ofprecast components considering resource constraints. Comput. Int. Manuf. Syst. 27, 2248–2259. https://doi.org/10.13196/j.cims.2021.08.008 (2021).

3. Saric, F., Begusic, S., Mercep, A. & Kostanjcar, Z. Statistical arbitrage portfolio construction based on preference relations. Expert Syst. Appl. 238, 1–12. https://doi.org/10.1016/j.eswa.2023.121906 (2023).

4. Chen, Y., Zhong, J., Feng, L. & Zhang, J. An adaptive archive-based evolutionary framework for many-task optimization. IEEE Trans. Emerg. Top. Comput. Intell. 4, 369–384. https://doi.org/10.1109/TETCI.2019.2916051 (2020).

5. Pradhan, D., Wang, S., Ali, S., Yue, T. & Liaaen, M. CBGA-ES+: A cluster-based genetic algorithm with non-dominated elitist selection for supporting multi-objective test optimization. IEEE Trans. Softw. Eng. 47, 86–107. https://doi.org/10.1109/TETCI.2019.2916051 (2021).

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