Research on Particle Swarm Optimization Algorithm Based on Cloud Computing Task Scheduling
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 algorithms 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.
Cloud computing Task scheduling Particle swarm optimization algorithm Swarm intelligence algorithm Correlation
Qing WANG Xue-liang FU Gai-fang DONG Sha-sha ZHAO Yan XU
College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot,Inner Mongolia 010020, China
国际会议
上海
英文
278-284
2018-04-15（万方平台首次上网日期，不代表论文的发表时间）