一种基于云环境下蚁群优化算法的改进研究

摘要

在对标准蚁群优化算法深入分析的基础上,结合云环境下的资源调度特性和遗传算法所具有的全局收敛快速的优点,引入了逆转变异策略,科学地将遗传算法融入到标准蚁群优化算法的每一次迭代过程中,很好地解决了标准蚁群优化算法容易陷入搜索速度慢和局部最优的缺陷.云环境下的模拟仿真对比实验结果表明,改进的蚁群优化算法不但能使云环境下的寻优能力大幅度提高,而且能够缩短系统任务平均运行时间,提升云计算环境下资源的效用.%In the standard ant colony optimization algorithm for in-depth analysis, Combined with the cloud environment and genetic characteristics of resource scheduling algorithm having the advantage of fast global convergence, the introduction of a reversal mutation strategy, the scientific standard genetic algorithm into ant swarm optimization algorithm in each iteration, solves defects that the standard ant colony optimization algorithm is easy to fall into local optimum and slow search speed. And through the cloud environment simulation control experiments, the results show that the improved ant colony optimization algorithm not only makes the cloud environment optimization capabilities increased significantly, but also reduces the average running time of the system task to enhance the utility of resources under the cloud computing environment.