基于 SVM-ACO 算法在云计算数据库中的访问研究

摘要

It has always been the focus of study as how to quickly search the needed data from database ,and there is some similar correlation between how the ants find their food in the ant colony optimization of artificial intelligence algorithms and how cloud computing nodes find and access database .Therefore ,both the introduction of ant colony algorithm into cloud computing database and the introduction of the chaos function in the pheromone update make the improved pheromone avoid the possibility of getting into local convergence ,and after getting the preliminary optimal solutions ,improve the optional so‐lution by introducing penalty factors in SVM and test the algorithm by supporting multi‐tree model of Map/Reduce .The sim‐ulation results show that the algorithm in this paper has been significantly improved in terms of cloud computing network queries and database consumption ,and greatly improved the efficiency of cloud computing .%如何能够能够快速地从数据库中寻找所需要的数据一直以来都是研究的重点，人工智能算法中的蚁群算法中的蚂蚁寻找食物与云计算节点寻找访问数据库具有类似的相关性。因此在云计算数据库中引入蚁群算法，在算法的信息素的更新中引入混沌函数，使得改进后的信息素避免陷入局部收敛的可能性，在获得初步的查询结果之后，通过引入 SVM中的惩罚因子，对查询结果进行优化，通过支持 Map／Reduce 的多叉树模型中进行算法检验。仿真实验证明，论文算法在云计算数据库中的查询和网络消耗方面相有了明显提高，从而在很大程度上提高了云计算的效率。