Hybrid MFO-PSO and Genetic Algorithms for Optimized Task Scheduling in Cloud-Enabled Smart Healthcare Systems - Natarajan, Durai Rajesh; Kethu, Sai Sathish; Valivarthi, Dharma Teja; Peddi, Sreekar; Narla, Swapna (Journal of ISMAC, 2025)
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.
Keywords: Task Scheduling; Cloud Computing; MFO; PSO; Genetic Algorithms; IoT; Healthcare; Optimization; Resource Allocation; Real-Time; Performance; Hybrid Algorithm