针对云计算环境下的任务调度优化问题和传统离散粒子群优化( DPSO) 算法早熟、精度低等缺点，提出了一种适合云计算环境下动态调整惯性权重因子的方法，并给出了云计算环境下改进后的离散粒子群优化算法． 该算法能快速确定合适的并行任务分配方案，使其达到调度长度最短的优化目标． 仿真结果表明: 文中改进的 DPSO 算法的收敛性、前期全局搜索和后期局部探索性能均优于传统的 DPSO 算法和遗传算法; 在任务数较大的情况下，采用改进 DPSO 算法的并行任务调度算法的调度长度明显优于采用传统DPSO 算法和遗传算法的并行任务调度算法．

Aiming at the optimization problem of task scheduling in the cloud computing environment and the defects of prematurity and low precision of traditional discrete particle swarm optimization (DPSO) algorithms，a
method of dynamically adjusting the inertia weight factor is proposed in a cloud computing environment，and an improved discrete particle swarm optimization algorithm is put forward． This algorithm can determine the appropriate parallel task allocation scheme quickly，and makes the scheme achieve the shortest scheduling length． Simulation results show that the improved DPSO algorithm is superior to the traditional DPSO algorithm and the genetic algorithm in terms of the convergence，the previous global search capability and the late local exploration performance，and that，in the case of a large number of tasks，the parallel task scheduling algorithm using the improved DPSO algorithm is superior to those using the traditional DPSO algorithm or the genetic algorithm in terms of scheduling length．