Abstract: Particle swarm optimization (PSO) is a popular intelligent algorithm to solve the task scheduling optimization problem of work-flow system in cloud computing environment. However, this algorithm is easy to fall into the local optimality. It is the reason that the execution time and cost of the scheduling scheme are higher than other methods. Therefore, by improving the calculation method of the single particle success value, the traditional adaptive inertia weight particle group task scheduling algorithm is optimized. Through each particle fitness and local optimal value and global optimal value that divided into four cases to compare, the inertia weight improved can be used to adjust the particle velocity more accurately. It can better equilibrate search capacity of particles between global and local, and avoid the local maximum of the particles. In this paper, we more accurately describe the particle state and improve the inertia weight. We can get a scheduling scheme with lower execution time and lower cost. The analog results show that the improved algorithm is stable. The convergence accuracy is obviously improved. It can effectively avoid prematurely falling into the local optimality.