Research on DAG parallel task scheduling problem based on quantum-behaved particle swarm optimization
基于量子粒子群优化的DAG并行任务调度研究

Task scheduling is one of the important problems in parallel computing system.This paper proposed a quantum-behaved particle swarm optimization algorithm for task scheduling based on directed acyclic graph.First redefined the parallel task scheduling problem and its aim.Then discussed the representation of the encoding, the procedure of the decoding, the computational method of position vector, the continuative of the discrete problem and the structure of the algorithm respectively.In the end,presented the algorithm simulation,experiment result analysis and the conclusions.The simulation results show that this algorithm has better global optimizing ability and more rapid convergence, and it is superior to genetic algorithm and particle swarm optimization algorithm.