摘要
讨论利用蚁群算法解决云计算资源的调度问题。蚁群算法利用正反馈机制加快了收敛速度,但同时具有易早熟,易陷入局部最优解等不足。针对此问题,提出用遗传算法优化蚁群优化算法,同时引入最大最小蚁群系统改进基本蚁群算法,从而形成新的遗传蚁群算法。实验结果表明,新算法应用于云计算资源调度中,能有效地缩短调度所用的平均时间,提高调度效率。
This paper discusses the use of ant colony algorithm to solve cloud computing resources scheduling problem. Ant colony algorithm uses positive feedback mechanism to speed up the convergence, but also has the disadvantages of easy precocity, or fall into local optimal solution. This paper puts forward the Genetic Algorithms（GA） to optimize ant colony optimization algorithm, while introduction of the Max-Min Ant System（MMAS） to improve the basic ant colony algorithm and forms a new genetic-ant colony algorithm（GA-ACO）. The experimental results show that, the new algorithm in the application of cloud computing resource scheduling problem, can effectively shorten the average scheduling time, and improve the system efficiency.
出处
《成都信息工程学院学报》
2013年第2期109-113,共5页
Journal of Chengdu University of Information Technology
基金
四川省科技计划资助项目(2012GZ0111)
关键词
计算机应用技术
人工智能
遗传算法
最大最小蚁群系统
云资源调度
遗传蚁群算法
computer application technology
artificial intelligence
genetic algorithms
max-rain ant system
cloud computing schedule
genetic-ant colony algorithm
