改进的猴群算法在云计算资源分配中的研究

如何能够更好的解决云计算资源分配问题一直都是研究的热点, 引入猴群算法, 针对猴群算法中出现的局部收敛速度快, 容易造成局部最优值的缺点, 首先在猴群算法中引入混沌算法和反向学习来初始化猴群的初始位置, 其次, 通过猴群算法中的爬, 望, 跳三个过程的改进使得改算法收敛精度提高. 通过经典函数测试后, 本文算法相比其他智能算法的性能有了明显的改进. Cloudsim平台证明将本文算法运用到云计算资源分配中, 在任务完成时间, 资源消耗方面都有了很大的提高.

Resource Allocation in Clouding Computing Based on Monkey Algorithm

It has always been the focus of research as how to solve the resource distribution problem in cloud computing, and aiming at the weakness emerging in the monkey algorithm, i.e. too quick convergence speed and being easy to fall into local optimal, etc. the monkey algorithm is introduced in this paper. First, chaos algorithm and inverse learning are introduced into monkey algorithm to initialize the initial position of monkey swarm. Second, the improvement of climbing, watching and jumping in monkey algorithm has improved the convergence precision of this algorithm. After the test of classic function, algorithm in this paper has improved noticeably in performance compared with other intelligent algorithms. Cloudsim platform has proved that by applying algorithm in this paper to cloud computing, resource consumption has been greatly improved during completing tasks.