Task Scheduling Based on Improved Simulated Annealing Algorithm
HU Cheng-song · Jisuanji fangzhen · 2011
The task of scheduling problems was studied.Q-learning algorithm based on the traditional learning algorithms for task scheduling has slow convergence speed,and the paper presented a simulated annealing-based Q-learning algorithm to improve the convergence speed.Task scheduling algorithm for object model was established,and the analysis of the Q-learning algorithm based on simulated annealing algorithm was introduced.Combined with the greedy strategy and the filtrating and determining in the state space,and the whole process of scheduling was given.Simulation results show that Q-learning tasks with a single scheduling algorithm significantly improves the convergence rate and shortens the execution time.