Journal of ISMAC
IRO Journals
Volume 6 — Issue 4 — December 2024

Advanced Task Scheduling in Cloud Healthcare Systems with Hybrid MFO-PSO and Artificial Bee Colony Optimization

https://doi.org/10.36548/jismac.2024.4.006
Pages: 374-394
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Vijaykumar Mamidala
Conga (Apttus), Broomfield, CO, USA
Thirusubramanian Ganesan
Cognizant Technology Solutions, Texas, USA
Mohanarangan Veerappermal Devarajan
Ernst & Young (EY), Sacramento, USA
Akhil Raj Gaius Yallamelli
Amazon Web Services Inc, Seattle, USA
Rama Krishna Mani Kanta Yalla
Amazon Web Services, Seattle, WA, USA
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How to Cite

Mamidala, Vijaykumar, Thirusubramanian Ganesan, Mohanarangan Veerappermal Devarajan, Akhil Raj Gaius Yallamelli, and Rama Krishna Mani Kanta Yalla. 2025. “Advanced Task Scheduling in Cloud Healthcare Systems With Hybrid MFO-PSO and Artificial Bee Colony Optimization”. Journal of ISMAC 6 (4): 374-94. https://doi.org/10.36548/jismac.2024.4.006.
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Keywords

— Task Scheduling
— Cloud Healthcare
— MFO
— PSO
— ABC
— Hybrid Optimization
— Real-Time Adaptation
— Resource Management
Published: 08-02-2025

Abstract

Efficient task scheduling in cloud healthcare systems is essential for handling large-scale data, optimizing resource utilization, and enhancing system performance. This research presents a hybrid optimization approach, MFO-PSO-ABC, integrating Moth Flame Optimization (MFO), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC) algorithms to address the complexities of dynamic workloads and resource constraints. The proposed hybrid method demonstrates superior performance in accuracy, efficiency, and resource utilization compared to individual algorithms, significantly improving task scheduling and system adaptability in real-time cloud healthcare environments.

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