Grid Task Scheduling Based on Advanced No Velocity PSO

In computational grid environment, the task scheduling problem can be stated as finding a schedule scheme for a series of tasks to be executed on multiple resources, so that the task completion time can be minimized. There have been a lot of researches on scheduling algorithm, and heuristic approach have played very good role. For example, Genetic Algorithms, Simulated Annealing Algorithm, Ant Colony Optimization Algorithm and Particle Swarm Optimization Algorithm all have been applied to the scheduling problem. Particle Swarm Optimization algorithm has been shown good performance in many areas. Some experiments showed that it is better than Genetic Algorithms. In this paper, an efficient task scheduling method based on an advanced no velocity Particle Swarm Optimization is proposed. Simulation results in comparing the advanced no velocity Particle Swarm Optimization method and Ant Colony Optimization Algorithm are presented.