云环境中蚁群遗传混合算法的资源调度研究
首发时间:2011-02-25
摘要:本文针对蚁群优化算法(ACO)的缺陷,考虑到云计算的特点,结合遗传算法全局收敛的优点,将遗传算法融入到蚁群优化算法的每一次迭代中,加快其收敛速度,并引入逆转变异策略,避免了蚁群优化算法陷入局部最优。深入研究了云环境中蚁群遗传算法的资源调度策略,并通过扩展云计算仿真平台CloudSim实现了模拟仿真。实验结果表明,此融合算法能够缩短云环境下的任务运行时间,提高资源利用率.
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Research on Ant Colony Optimization and Genetic Algorithm Resource Schedule Strategy in the Cloud Environment
Abstract:For characteristics of Ant Colony Optimization Algorithm, thinking over the characteristics of the Cloud Computing, the global fast convergence of genetic algorithm is utilized to combine ant colony optimization algorithm with genetic algorithm in each generation,which enhances the convergence rate and improves the efficiency . And the reversal variation strategy is introduced to avoid the ant colony optimization algorithm falling into partial most superior. The paper deeply researches the improved Ant Colony Optimization Algorithm (ACO) and genetic algorithm in resources scheduling strategy of the cloud computing, by extending the Cloud Computing platform CloudSim to test the simulation.The results show that this method can reduce the task average running time, and raises the rate availability of resources.
Keywords: ant optimization algorithm genetic algorithm cloud environment resource schedule
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