Task Scheduling in Cloud Computing Using Harris-Hawk Optimization

This paper presents a simulation of the Harris-Hawk Optimization (HHO) algorithm, which aims to minimize the makespan of a specified task set in a cloud computing environment. The algorithm is inspired by the team association of hawks in hunting and escaping prey. It has gained significant attention from researchers due to its effectiveness in solving real-world problems across different applications. As a result, the HHO algorithm has been widely applied to various optimization problems. In this study, the proposed HHO algorithm is simulated and compared with other well-known swarm intelligence algorithms, including Bat Algorithm (BA), Grey Wolf Optimization (GWO), and Particle Swarm Optimization (PSO). The simulation results demonstrate that the HHO algorithm surpasses the other three in producing better results. Furthermore, given the HHO algorithm's reliability in solving single-objective problems, this study further validates its effectiveness in addressing various optimization concerns.