云计算任务调度双精英种群文化基因改进算法

A Cloud Computing Task Scheduling Algorithm Based on Improved Memetic Algorithm Using Dual Elite Subpopulations

云计算任务调度是一个难于精确求解的调度问题,需要兼顾考虑计算时间和资源利用率。为了最大程度地节省计算时间和提升资源利用率,论文提出了一种云计算任务调度双精英种群文化基因改进算法。首先,针对文化基因算法全局搜索能力不高的问题,通过结合遗传算法与粒子群算法,提出一种混合全局搜索策略,提高算法的种群多样性和收敛速度。其次,为改善普通精英种群在迭代后期进化缓慢的缺点,采用了双种群的进化机制。具体的Matlab仿真实验结果表明,改进算法的优化性能更佳。

Cloud computing task scheduling is a scheduling problem difficult to be solved accurately,and both complete time and resource utilization rate need to be considered.In order to shorten the time to complete the task and improve resource utilization rate,an improved memetic algorithm(IMA)for cloud computing task scheduling is proposed by using dual elite subpopulations.Firstly,in order to improve the performance of the global search,the strong diversity maintained ability of the genetic algorithm is combined with the quick convergence speed of the particle swarm algorithm.Secondly,to improve the problem of the slow evolution of the general elite population in iterative computation process,dual elite subpopulations is used.The specific Matlab simulation re⁃sults show that the improved algorithm has better optimization performance.