摘要
云计算资源调度是一个极其复杂的NP问题,不易求解。为缩短任务完成时间,文章提出了一种双粒子群的粒子群改进算法,并将其应用于云资源调度。首先,在惯性权重线性递减的基础上,加入随机数扰动,使惯性权重大幅增大,以便于跳出局部搜索,进行全局搜索,从而防止局部收敛;其次,针对粒子群算法在迭代后期进化减慢的缺点,采用了一种双粒子群的寻优机制,以便于更好地保持粒子群多样性。最后,在Matlab GUI平台下采用几种不同的粒子群算法进行仿真试验。仿真结果表明,在相同条件下改进的粒子群算法能够寻到更精确的解。
Cloud computing resource scheduling is a complex NP problem, and is difficult to solve it. In order to shorten the time to complete the task, an improved particle swarm optimization（PSO）algorithm is proposes by using linear decreasing inertia weight PSO algorithm in cloud resource scheduling. Based on the linear decreasing inertia weight, the constant disturbance is added to increase the inertia weight, so as to get rid of the local search and get the global search. Meanwhile, in order to avoid the PSO algorithm particle height gathered around the optimal particle, the particle tends to be identical, so as to greatly damage the diversity of particle swarm, and it has adaptive probability inertia weight with random individuals. Thus, it can better maintain the diversity of the population. Finally, the improved PSO algorithm can get a more accurate solution under the same condition.
作者
吴宇星
刘媛华
WU Yuxing;LIU Yuanhua(Management School,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处
《物流科技》
2018年第11期12-15,18,共5页
Logistics Sci-Tech
关键词
云计算
资源调度
粒子群
惯性权重递减
随机数扰动
cloud computing
resource scheduling
particle swarm optimization（PSO）
linear decreasing inertia weight
random number disturbance
