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Task Scheduling In Cloud Computing Using Harris-Hawk Optimization
This paper presents the implementation of the Harris-Hawk Optimization (HHO) algorithm in minimizing the makespan of the specified task set in a cloud computing environment. The algorithm imitates the action of the hawk's team collaboration in hunting and fleeing prey. As a result, the algorithm has received widespread attention among researchers regarding its performance in dealing with further applications in real-world problems. This increased interest has led to the advent of HHO applications in many optimization problems. Given the strength of this emerging algorithm in solving single-objective problems, this paper simulates the performance of the proposed HHO against the other well-known swarm intelligence algorithms such as Bat Algorithm (BA), Grey Wolf Optimization (GWO), and Particle Swarm Optimization (PSO). The simulation results demonstrate that the HHO algorithm produces better outcomes than the three other swarm algorithms.