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．