Volume - 7 | Issue - 1 | march 2025
Hybrid MFO-PSO and Genetic Algorithms for Optimized Task Scheduling in Cloud-Enabled Smart Healthcare Systems
Open Access
139
Pages: 1-17
Cite this article
Natarajan, Durai Rajesh, Sai Sathish Kethu, Dharma Teja Valivarthi, Sreekar Peddi, and Swapna Narla. "Hybrid MFO-PSO and Genetic Algorithms for Optimized Task Scheduling in Cloud-Enabled Smart Healthcare Systems." Journal of IoT in Social, Mobile, Analytics, and Cloud 7, no. 1 (2025): 1-17
Published
17 February, 2025
Abstract
Full Article PDF
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.
KeywordsTask Scheduling Cloud Computing MFO PSO Genetic Algorithms IoT Healthcare Optimization Resource Allocation Real-Time Performance Hybrid Algorithm
