In order to improve the adaptive management ability of virtual machine placement in cloud computing, an adaptive management and multi-objective optimization method for virtual machine placement in cloud computing is proposed based on particle swarm optimization (PSO). The objective optimization model of adaptive management of virtual machine placement in cloud computing is constructed by particle swarm evolution, and the global optimization control of adaptive management of virtual machine placement in cloud computing is carried out by introducing extremum perturbation operator. The global dynamic objective function of particle swarm optimization is constructed, and the global optimal solution of virtual machine in cloud computing is found by deconvolution algorithm, and the optimal position of particle swarm is searched in two-dimensional space. The multi-objective optimization problem of adaptive management of virtual machine placement is transformed into particle swarm optimization problem to realize adaptive management and multi-objective optimization of virtual machine placement in cloud computing. Simulation results show that the adaptive management of virtual machine placement in cloud computing using this method has better global optimization ability, better convergence of particle swarm optimization, and better performance of multi-objective optimization.

为了提高云计算中虚拟机布局的自适应管理能力，提出了一种基于粒子群优化算法的云计算中虚拟机布局的自适应管理和多目标优化方法。通过粒子群演化算法构建了云计算中虚拟机布局自适应管理的目标优化模型，引入极值摄动算子对云计算中虚拟机布局自适应管理的全局优化控制。构建了粒子群优化算法的全局动态目标函数，并通过反卷积算法在云计算中找到了虚拟机的全局最优解，并在二维空间中搜索了粒子群的最优位置。将虚拟机布局的自适应管理的多目标优化问题转化为粒子群优化问题，以实现云计算中虚拟机布局的自适应管理和多目标优化。仿真结果表明，使用该方法对云计算中的虚拟机位置进行自适应管理具有更好的全局优化能力，更好的粒子群优化收敛性和更好的多目标优化性能。