Advanced Task Scheduling in Cloud Healthcare Systems with Hybrid MFO-PSO and Artificial Bee Colony Optimization - Mamidala, Vijaykumar; Ganesan, Thirusubramanian; Devarajan, Mohanarangan Veerappermal; Yallamelli, Akhil Raj Gaius; Yalla, Rama Krishna Mani Kanta (Journal of ISMAC, 2025)
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.