To address the problems of single scheduling algorithms and wasted processor resources for signal processing tasks in current heterogeneous platforms,a load balancing scheduling algorithm with Q-learning enhanced ant colony algorithm for heterogeneous systems is proposed.The algorithm is designed to prioritize tasks by a triage sorting method for the different needs of computation-inten-sive and communication-intensive tasks.Q-learning and ant colony algorithms are mapped to task scheduling in heterogeneous signal processing platforms through scenario adaptation.The Q-table is dynamically computed using the reward function and is used as the initial pheromone of the ant colo-ny algorithm,which speeds up the convergence of the ant colony.The load matrix is designed to dy-namically adjust system load balancing according to the real-time load on the processor.Pseudo-ran-dom scaling rules are used to make processor selections.Tasks are assigned by creating a schedule list with constraint relationships between tasks.Finally,simulation experiments are performed with randomly generated directed acyclic graphs.The results show significant improvements in both the reduction of the maximum completion time(scheduling length)and the increase in processor utiliza-tion.
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