Journal of ISMAC
IRO Journals
Volume 7 — Issue 1 — March 2025

Hybrid MFO-PSO and Genetic Algorithms for Optimized Task Scheduling in Cloud-Enabled Smart Healthcare Systems

https://doi.org/10.36548/jismac.2025.1.001
Pages: 1-17
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Durai Rajesh Natarajan
Estrada Consulting Inc, California, USA
Sai Sathish Kethu
NeuraFlash, Georgia, USA
Dharma Teja Valivarthi
Tek Leaders, Texas, USA
Sreekar Peddi
Tek Leaders, Texas, USA
Swapna Narla
Tek Yantra Inc, California, USA
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How to Cite

Natarajan, Durai Rajesh, Sai Sathish Kethu, Dharma Teja Valivarthi, Sreekar Peddi, and Swapna Narla. 2025. “Hybrid MFO-PSO and Genetic Algorithms for Optimized Task Scheduling in Cloud-Enabled Smart Healthcare Systems”. Journal of ISMAC 7 (1): 1-17. https://doi.org/10.36548/jismac.2025.1.001.
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Keywords

— Task Scheduling
— Cloud Computing
— MFO
— PSO
— Genetic Algorithms
— IoT
— Healthcare
— Optimization
— Resource Allocation
— Real-Time
— Performance
— Hybrid Algorithm
Published: 17-02-2025

Abstract

A hybrid MFO-PSO-GA algorithm is presented for optimizing scheduling in cloud-based smart healthcare systems. By combining Moth Flame Optimization, Particle Swarm Optimization, and Genetic Algorithms, this solution improves resource consumption, reduces execution time, and provides real-time responsiveness. Experimental results demonstrate superior performance in accuracy, precision, recall, and resource allocation efficiency at 95%. This solution enhances scalability, security, and performance, offering a robust framework for cloud-based healthcare task scheduling.

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