Research on Particle Swarm Optimization Algorithm Based on Cloud Computing Task Scheduling

Abstract

Whether home or abroad, both resource allocation and task scheduling is one of the popular problems in the cloud computing environment, and swarm intelligence algorithm is a hot topic. Particle swarm optimization (PSO) is an important swarm intelligent algorithm in solving the task scheduling optimization problem. Based on the research of the basic PSO algorithm, this paper proposes the next improvement strategies: (1) The calculation method of the success value of single particle is improved, and the progressive speed of individual particles is improved; (2) Integrated the correlation between random factors, that improved the global optimization ability of PSO algorithm in the process of optimization, and avoided the particle trapping into the local best. The simulation results show that under the same conditions, the improved algorithm’s execution time is better than the sequential scheduling algorithm, the greedy algorithm, the basic PSO algorithm, the correlation PSO algorithm and the adaptive PSO algorithm for inertia weights.