Lei Yin1, Chang Sun2, Ming Gao3, Yadong Fang4, Ming Li1, Fengyu Zhou1,*
Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1587-1608, 2023, DOI:10.32604/iasc.2023.039380
Abstract The solution strategy of the heuristic algorithm is pre-set and has good performance in the conventional cloud resource scheduling process. However, for complex and dynamic cloud service scheduling tasks, due to the difference in service attributes, the solution efficiency of a single strategy is low for such problems. In this paper, we presents a hyper-heuristic algorithm based on reinforcement learning (HHRL) to optimize the completion time of the task sequence. Firstly, In the reward table setting stage of HHRL, we introduce population diversity and integrate maximum time to comprehensively determine the task scheduling and the More >
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