Modified firefly algorithm for workflow scheduling in cloud-edge environment-Reference-Cited by-同舟云学术

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Modified firefly algorithm for workflow scheduling in cloud-edge environment

Published:2022-02-02 Issue:11 Volume:34 Page:9043-9068
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Bacanin Nebojsa,Zivkovic Miodrag,Bezdan Timea,Venkatachalam K.,Abouhawwash Mohamed

Funder

ministry of science of republic of serbia

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

https://link.springer.com/content/pdf/10.1007/s00521-022-06925-y.pdf

Reference52 articles.

1. Aggarwal A, Dimri P, Agarwal A, Bhatt A (2020) Self adaptive fruit fly algorithm for multiple workflow scheduling in cloud computing environment. Kybernetes

2. Bacanin N, Bezdan T, Tuba E, Strumberger I, Tuba M, Zivkovic M (2019a) Task scheduling in cloud computing environment by grey wolf optimizer. In 2019 27th Telecommunications Forum (TELFOR) (pp. 1–4). IEEE

3. Bacanin N, Tuba E, Bezdan T, Strumberger I, Tuba M (2019) Artificial flora optimization algorithm for task scheduling in cloud computing environment. In: Yin H, Camacho D, Tino P, Tallón-Ballesteros AJ, Menezes R, Allmendinger R (eds) Intelligent Data Engineering and Automated Learning—IDEAL 2019. Springer International Publishing, Cham, pp 437–445. https://doi.org/10.1007/978-3-030-33607-3_47

4. Bacanin N, Tuba E, Zivkovic M, Strumberger I, Tuba M (2019c) Whale optimization algorithm with exploratory move for wireless sensor networks localization. In International Conference on Hybrid Intelligent Systems (pp. 328–338). Springer

5. Basha J, Bacanin N, Vukobrat N, Zivkovic M, Venkatachalam K, Hubálovskỳ S, Trojovskỳ P (2021) Chaotic harris hawks optimization with quasi-reflection-based learning: an application to enhance cnn design. Sensors 21:6654

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