A Proposed Model for Enhancing the Performance of Health Care Services in Smart Cities Using Hybrid Optimization Techniques

Technological development represents an excellent opportunity for the growth and spread of smart healthcare services based on cloud computing and the Internet of things. Cloud computing allows users to access computing resources such as applications, software, and hardware anywhere at any time to serve them effectively. One of the most significant issues facing smart cloud-based healthcare services is task scheduling, which is responsible for allocating resources to users' requests in a way that distributes the workload equitably using one of the optimization techniques. Swarm intelligence is one of the most important used optimization algorithms recently to find an optimal solution for solving the task scheduling problem that assigns the users' tasks to multiple computing resources. This paper presents and implements a hybrid swarm optimization algorithm using two different intelligent swarm algorithms, which are Moth Flame Optimization (MFO) method and Particle Swarm Optimization (PSO). The proposed model was implemented and tested using the Cloudsim simulation framework. Output values are compared to the most popular optimization algorithms that were previously used, such as ant colony optimization (ACO), PSO, and MFO. The simulation results reveal that HPSOMFO outperforms all other existing algorithms by decreasing the makespan and waiting time of medical requests and increasing resource utilization.