模糊云资源调度的CMA-PSO算法

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本站小编 Free考研考试/2024-10-07

作者:李成严，宋月，马金涛
Authors:LI Cheng-yan,SONG Yue,MA Jin-tao摘要:摘要:针对多目标云资源调度问题，以优化任务的总完成时间和总执行成本为目标，采用模糊数学的方法，建立了模糊云资源调度模型。利用协方差矩阵能够解决非凸性问题的优势，采取协方差进化策略对种群进行初始化，并提出了一种混合智能优化算法CMA-PSO算法(covariance matrix adaptation evolution strategy particle swarm optimization，CMA-PSO )，并使用该算法对模糊云资源调度模型进行求解。使用Cloudsim仿真平台随机生成云计算资源调度的数据，对CMA-PSO算法进行测试，实验结果证明了CMA-PSO算法对比PSO算法(particle wwarm optimization)，在寻优能力方面提升28%，迭代次数相比提升20%，并且具有良好的负载均衡性能。
Abstract:Abstract:Aiming at the multiobjective cloud resource scheduling problem, with the goal of optimizing the total completion time and total execution cost of the task, a fuzzy cloud resource scheduling model is established using the method of fuzzy mathematics.Utilizing the advantage of the covariance matrix that can solve the non-convexity problem, adopting the covariance evolution strategy to initialize the population, a hybrid intelligent optimization algorithm CMA-PSO algorithm (covariance matrix adaptation evolution strategy particle swarm optimization，CMA-PSO) is proposed to solve the fuzzy cloud resource scheduling model.The Cloudsim simulation platform was used to randomly generate cloud computing resource scheduling data, and the CMA-PSO algorithm was tested.The experimental results showed that compared with the PSO algorithm (particle swarm optimization), the optimization capability of CMA-PSO algorithm is increased by 28%, the number of iterations of CMA-PSO algorithm is increased by 20%, and it has good load balancing performance.

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模糊云资源调度的CMA-PSO算法

本站小编 Free考研考试/2024-10-07

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