The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resource...
详细信息
The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resources for optimized resource utilization. Several meta-heuristic algorithms have shown effectiveness in task scheduling, among which the relatively recent willowcatkinoptimization (WCO) algorithm has demonstrated potential, albeit with apparent needs for enhanced global search capability and convergence speed. To address these limitations of WCO in cloud computing task scheduling, this paper introduces an improved version termed the Advanced willowcatkinoptimization (AWCO) algorithm. AWCO enhances the algorithm’s performance by augmenting its global search capability through a quasi-opposition-based learning strategy and accelerating its convergence speed via sinusoidal mapping. A comprehensive evaluation utilizing the CEC2014 benchmark suite, comprising 30 test functions, demonstrates that AWCO achieves superior optimization outcomes, surpassing conventional WCO and a range of established meta-heuristics. The proposed algorithm also considers trade-offs among the cost, makespan, and load balancing objectives. Experimental results of AWCO are compared with those obtained using the other meta-heuristics, illustrating that the proposed algorithm provides superior performance in task scheduling. The method offers a robust foundation for enhancing the utilization of cloud computing resources in the domain of task scheduling within a cloud computing environment.
The willow catkin optimization algorithm (WCO) is a newly proposed meta-heuristic algorithm in recent years that has a simple structure and excellent optimization searching ability, but the WCO algorithm could benefit...
详细信息
The willow catkin optimization algorithm (WCO) is a newly proposed meta-heuristic algorithm in recent years that has a simple structure and excellent optimization searching ability, but the WCO algorithm could benefit from improvements in both convergence speed and solution diversity. In this paper, the parallel technology is introduced into the WCO algorithm, and by proposing two new communication strategies, the Random Mean (RM) method and the Optimal Flight (OF) method, the goal is to utilize all solution information obtained by each subpopulation in the parallel strategy to enhance the algorithm's performance. Additionally, the WCO algorithm has been hybridized with the Differential Evolution algorithm (DE), and a mutation mechanism has been introduced to improve the diversity of solutions. The resulting algorithm is called the Hybrid Parallel willow catkin optimization algorithm (HPWCO). In this paper, the HPWCO algorithm is tested on the CEC2017 benchmark function set and applied to five real-world engineering optimization problems with constraints, and the experimental results were compared with three types of algorithms: the classical algorithm, the newly proposed algorithm, and the parallel algorithm. The results indicate that the HPWCO performs excellently.
暂无评论