摘要
云计算资源调度是云计算中一个关键且复杂的调度问题,需要考虑众多的因素.为减少任务完成时间,本文提出了一种云资源调度粒子群改进算法.首先,本文在惯性权重线性递减的基础上,加入了混沌随机数扰动,使惯性权重有概率的适度增加,以便于跳出局部搜索,进行全局搜索;其次,针对粒子群算法和蚁群算法都容易陷入局部最优的缺点,结合粒子群算法和蚁群算法的优化策略,提出了一种改进的混合优化策略.其仿真结果及实际算例测试结果表明,在相同条件下改进算法能够寻到更精确的解.
Cloud computing resource scheduling is a key and complex scheduling problem in cloud computing,and many factors need to be considered.In order to reduce the time of cloud computing,an Improved Particle Swarm Optimization (IPSO) algorithm is proposed.Based on the linear decreasing inertia weight,the chaotic constant disturbance is added to increase the inertia weight with little probability,so as to get rid of the local search and get the global search.Meanwhile,in order to solve the defect that the two algorithms fall into partial optimization easily,the proposed algorithm combines the optimization strategy of particle swarm optimization and ant colony optimization.The Matlab simulation and the testing of practical examples results show that the improved algorithm can get a more accurate solution under the same condition.
作者
罗云
唐丽晴
LUO Yun;TANG Li-Qing(Department of Computer Application, China Coast Guard Academy, Ningbo 315801, China)
出处
《计算机系统应用》
2019年第7期151-156,共6页
Computer Systems & Applications
关键词
云计算
资源调度
粒子群
惯性权重递减
混沌随机数扰动
cloud computing
resource scheduling
Particle Swarm Optimization (PSO)
Linear Decreasing inertia Weight (LDW)
chaotic constant disturbance
