QUANTUM-ENHANCED RED DEER OPTIMIZATION FOR OPTIMIZING TASK SCHEDULING AND ENERGY EFFICIENCY IN CLOUD-BASED HEALTHCARE SYSTEMS

Abstract

Cloud computing has revolutionized the healthcare industry by providing scalable, secure, and cost-effective solutions for healthcare data management and applications. This article presents Quantum-Enhanced Red Deer Optimization (Q-RDO), a new hybrid optimization algorithm that combines the Red Deer Algorithm (RDO) and Quantum-Inspired Particle Swarm Optimization (QIPSO) to solve complex problems in cloud-based health care systems, including task scheduling, resource allocation, and energy optimization. The RDO element simulates red deer social and territory behaviour to permit an effective equilibrium between global exploration and local exploitation. As opposed to QIPSO, which applies quantum principles to strengthen global search ability in order to optimize the process and avoid premature convergence. Q-RDO is utilized to improve the optimization process in healthcare by optimizing real-time resource management. This helps to reduce the energy usage by 15%, enhances task scheduling efficiency by 21.2% and improves patient scheduling and resource allocation-RDO can efficiently handle the operational challenges exhibited by modern healthcare systems by improving sustainability and managing costs while ensuring improved patient results.