Cloud computing needs to manage a large number of computing resources, while resources scheduling strategy plays a key role in determining the efficiency of cloud computing. evolutionary algorithms (EA) as appropriate tools to optimize multi-objective problems have been applied to optimize Resources Scheduling of cloud computing ,However, studies on improving the convergence ratio and processing time in the most applied algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) in Resources Scheduling domains remain poorly understood. the resource schedule algorithmbased on Artificial Fish Swarm Optimization(AFSA) for Cloud Computing Environment has been designed and implemented after the study on the resource schedule of Cloud Computing. The main idea of improved AFSA is to extend Fish Swarm Optimization to the interacting swarms model by cooperative Models . The improved AFSA probability analysis indicates that searching solution is much more efficient and speeds up the multi-swarm to converge to the global optimum.Finally, the result of the experiment indicates that the scheduling system can improve the efficiency of dispatching resource and the utilization ratio in the Cloud Computing system.
