Study of cloud computing task scheduling problem, in view of many existing in the process of logistics transportation route choice question, to consider in a certain amount of time constraints, road clearance restriction, road comfort, and driving cost constraints under the condition of how to carry out the task allocation, to achieve the shortest path as the optimization goal, application of particle swarm optimization (PSO) algorithm to extend it a task scheduling method based on hybrid particle swarm optimization (PSO) cloud is proposed. Experimental results show that hybrid particle swarm optimization plays a good role in cloud computing task scheduling, which can shorten the distance to a certain extent, improve the efficiency, improve the applicability and scalability of the algorithm, not trapped in local optimization, and achieve the optimal global effect.

In this study, the task allocation mechanism in mobile swarm intelligence perception is studied and analyzed. Considering how to allocate tasks under specified time constraints and optimize for the goal, a hybrid artificial fish swarm algorithm is proposed by using swarm intelligence algorithm. The inertia index of particle swarm optimization algorithm is pulled into the typical artificial fish swarm algorithm, which is proved by simulation experiments. The hybrid artificial fish swarm algorithm greatly improves the convergence speed, avoids the shortage that the artificial fish swarm algorithm often stop when it obtains the local optimization, and this make the algorithm obtains the effect of global optimization.