考虑时变资源价格因素的Q学习强化和声搜索网格任务调度算法
Considering Time-Varying Factor with Q Learning Strengthen Harmony Search Algorithm for Grid Task Scheduling
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摘要: 针对传统网格任务调度算法不考虑价格时变因素缺点，提出一种基于Q学习强化和声搜索算法的考虑时变资源价格因素网格任务调度算法。首先，综合考虑价格时变因素影响，对网格任务调度模型进行改进，提出一种新的调度模型；其次，利用Q学习算法对和声搜索算法进行改进，平衡了算法的广度和深度搜索能力；最后，通过与同类算法的仿真对比结果表明，该算法和模型具有较好的收敛速度优化性能，并且在资源价格满意度和任务调度长度两个层面具有更全面的优化性能。Abstract: In order to solve the shortcoming of the traditional factors of grid task scheduling algorithm ,in which the time‐varying price is not considered ,the Q learning strengthen harmony search algorithm has beenpresented to do the grid task scheduling .Firstly ,considering the time‐varying price factors ,the grid task scheduling model has beenimproved and a new scheduling model proposed ;Secondly ,the Q learning algorithm has beenused to improve the harmony search algorithm , w hichhas been used for the scope search .And the Q learning algorithm has beenused for depth development ,which balances wide‐depth search ability of the algorithm ;Finally ,through the simulation results have beencompared with the similar algorithms show that ,the algorithm and performance optimization model has better convergence speed , which has the optimal performance of a more comprehensive in two aspects of resource price satisfaction and task scheduling length .
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