Aquila Optimizer Based PSO Swarm Intelligence for IoT Task Scheduling Application in Cloud Computing

This paper introduces IAO, a new swarm intelligence approach for addressing the challenge of task scheduling in cloud computing. The proposed method uses conventional Aquila Optimizer (AO) and Particle Swarm Optimizer (PSO) as a hybrid method based on a novel transition mechanism. The proposed hybrid method, IAO, combined the AO and PSO to avoid the weaknesses they face; these weaknesses are trapped in the local search area and have low solution diversity. The proposed transition mechanism is proposed to acquire proper changes between the search operators in order to keep the improvements; it changes between them when any algorithm gets stuck or the solutions diversity decreases. Several scenarios are conducted and tested to validate the suggested method's ability to address the task scheduling problem; these scenarios contain various tasks (i.e., 600, 1000, and 2000). The obtained results are compared with other well-known methods in terms of Max, Mean, Min of the Expected Complete Time (ECT), Friedman ranking test, and Wilcoxon signed-rank test. The proposed IAO method achieved better results and promising compared to other comparative methods; it is an excellent scheduling approach for solving any related scheduling problem.