An enhanced dung beetle optimizer for cloud task schedule

Rongxiang Xie; Shaobo Li; Fengbin Wu; Libang Wu; Xuan Xiong

doi:10.1117/12.3036506

21 July 2024 An enhanced dung beetle optimizer for cloud task schedule

Rongxiang Xie, Shaobo Li, Fengbin Wu, Libang Wu, Xuan Xiong

Proceedings Volume 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024); 132192J (2024) https://doi.org/10.1117/12.3036506
Event: 4th International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2024), 2024, Kaifeng, China

Abstract

Aiming to address the shortcomings of Dung Beetle Optimizer (DBO) in the areas of accuracy and convergence speed in dealing with cloud computing task scheduling issues, this paper suggests an advanced Dung Beetle Optimizer (DGTDBO)- based cloud computing task scheduling method by combining the differential strategy and the perturbation strategy. Among them, the differential strategy strengthens the global exploration performance of the method, while the perturbation strategy improves the capability of the algorithm to escape from the local optimal trap. Through validation on different sizes of cloud computing systems, it is confirmed that the suggested DGTDBO can enhance the solution accuracy and speed of the algorithm effectively. It is considered as a promising cloud scheduling method.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Rongxiang Xie, Shaobo Li, Fengbin Wu, Libang Wu, and Xuan Xiong "An enhanced dung beetle optimizer for cloud task schedule", Proc. SPIE 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024), 132192J (21 July 2024); https://doi.org/10.1117/12.3036506

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