An Improved Particle Swarm Optimization Algorithm Based on Adaptive Weight for Task Scheduling in Cloud Computing

Task scheduling(1) is a very important part of the cloud computing environment. Aiming at the characteristics of task scheduling and considering both users and cloud service providers, this paper proposes an improved particle swarm optimization algorithm based on adaptive weights. The algorithm uses adaptive weights to make the weight change with the increase of the number of iterations, and introduces random weights in the later stage, which avoids the situation that the particle swarm algorithm may be trapped in the local optimum when it comes to late stage. Applying the algorithm to task scheduling in cloud computing can achieve a better scheduling plan. The experiment results show that under the same conditions, the improved particle swarm optimization algorithm is better than the standard particle swarm optimization algorithm, which improves the using efficiency of resource while ensuring the task completion time.