Black eagle optimizer: a metaheuristic optimization method for solving engineering optimization problems-Reference-Cited by-同舟云学术

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Black eagle optimizer: a metaheuristic optimization method for solving engineering optimization problems

Published:2024-06-13 Issue: Volume: Page:
ISSN:1386-7857
Container-title:Cluster Computing
language:en
Short-container-title:Cluster Comput

Author:

Zhang Haobin,San Hongjun,Chen Jiupeng,Sun Haijie,Ding Lin,Wu Xingmei

Funder

Yunnan Province Basic Research Program Project

Kunming University of Science and Technology College Level Personnel Training Project

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s10586-024-04586-1.pdf

Reference79 articles.

1. Gao, G., Zhang, S., Na, J., Liu, F.: Industrial robot trajectory error compensation based on calibration and joint space interpolation. J. Mech. Eng. 57(21), 55–67 (2021)

2. Grebner, T., Janoudi, V., Schoeder, P., Waldschmidt, C.: Self-calibration of a network of radar sensors for autonomous robots. IEEE Trans. Aerosp. Electron. Syst. (2023). https://doi.org/10.1109/TAES.2023.3277427

3. Yang, Z., Chen, C., Huang, G.: A hybrid global optimization algorithm for GA-nonuniform kriging-gradient projection for optimal design of robots. J. Mech. Eng. 55(11), 61–68 (2019)

4. Koike, R., Ariizumi, R., Matsuno, F.: Simultaneous optimization of discrete and continuous parameters defining a robot morphology and controller. IEEE Trans. Neural Netw. Learn. Syst. (2023). https://doi.org/10.1109/TNNLS.2023.3272068

5. Kumar, R., Dhiman, G.: A comparative study of fuzzy optimization through fuzzy number. Int. J. Mod. Res. 1(1), 1–14 (2021)

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