دانشگاه صنعتی نوشیروانی بابل مجله علمی رایانش نرم و فناوری اطلاعات 2383-1006 14 1 2025 05 22 A Chaotic RIME Optimization Algorithm based on Generalized Quadratic Interpolation with Levy Flight for Task Scheduling in Cloud Computing الگوریتم بهینه‌سازی RIME آشوب‌ناک مبتنی بر درون‌یابی درجه دوم تعمیم‌یافته با پرواز لوی برای زمان‌بندی وظایف در رایانش ابری 57 67 220413 10.22034/jscit.2025.220413 FA محمدرضا صبی پور گروه مهندسی کامپیوتر، واحد بندرانزلی، دانشگاه آزاد اسلامی، بندرانزلی، ایران. 0009-0003-7361-9613 منیره حسینی صیادنورد گروه مهندسی کامپیوتر، واحد بندرانزلی، دانشگاه آزاد اسلامی، بندرانزلی، ایران. 0000-0003-0367-9517 Journal Article 2025 01 30 Task scheduling in cloud computing still remains a crucial issue regarding system performance and user satisfaction. As organizations increasingly rely on cloud infrastructure to manage and execute their computational tasks, the need for effective scheduling becomes paramount. The main purpose of task scheduling is to assign submitted tasks to available appropriate resources while maintaining the quality of service and service level agreement. An effective task scheduling algorithm must be able to reduce the makespan as a crucial performance metric in cloud computing systems. In this paper, we have introduced a new efficient task scheduling algorithm called GQIRIME based on the RIME optimization algorithm to reduce the makespan, cost, and total execution time of the cloud system. An enhanced exploitation strategy is proposed based on generalized quadratic interpolation and levy flight to increase convergence rate while maintaining robust search. Furthermore, the proposed algorithm is integrated with chaos mapping to get a more diversified initial population. We have evaluated the effectiveness of our proposed approach using the Cloudsim toolkit. The results analysis demonstrates that our proposed algorithm outperforms other methods and significantly improves key performance metrics in task scheduling. According to the experiment results, the proposed task scheduling algorithm achieved a lower cost, makespan, and total execution time. The results show a 29.3% improvement in terms of makespan, 61.8% improvement in cost, and 29.4% improvement in total execution time on average compared to counterparts. Task scheduling in cloud computing still remains a crucial issue regarding system performance and user satisfaction. As organizations increasingly rely on cloud infrastructure to manage and execute their computational tasks, the need for effective scheduling becomes paramount. The main purpose of task scheduling is to assign submitted tasks to available appropriate resources while maintaining the quality of service and service level agreement. An effective task scheduling algorithm must be able to reduce the makespan as a crucial performance metric in cloud computing systems. In this paper, we have introduced a new efficient task scheduling algorithm called GQIRIME based on the RIME optimization algorithm to reduce the makespan, cost, and total execution time of the cloud system. An enhanced exploitation strategy is proposed based on generalized quadratic interpolation and levy flight to increase convergence rate while maintaining robust search. Furthermore, the proposed algorithm is integrated with chaos mapping to get a more diversified initial population. We have evaluated the effectiveness of our proposed approach using the Cloudsim toolkit. The results analysis demonstrates that our proposed algorithm outperforms other methods and significantly improves key performance metrics in task scheduling. According to the experiment results, the proposed task scheduling algorithm achieved a lower cost, makespan, and total execution time. The results show a 29.3% improvement in terms of makespan, 61.8% improvement in cost, and 29.4% improvement in total execution time on average compared to counterparts. https://jscit.nit.ac.ir/article_220413_f2dd3c09f89ea35e45866c649fccee05.pdf