基于自适应免疫多态蚁群算法的云数据库动态路径优化研究  

Cloud database dynamic path optimization based on immune polymorphic ant colony algorithm

作　　者：高长元[1,2] 张云晖[1] 张树臣[1,2] 何晓燕[1,2] 

机构地区：[1]哈尔滨理工大学管理学院,哈尔滨150040 [2]哈尔滨理工大学高新技术产业发展研究中心,哈尔滨150040

出　　处：《计算机应用研究》2015年第10期2955-2959,共5页Application Research of Computers

基　　金：国家自然科学基金资助项目(71272191;71072085);黑龙江省自然科学基金资助项目(G201301);黑龙江省高等学校哲学社会科学创新团队建设计划资助项目(TD201203)

摘　　要：云数据库的分布性及动态性增加了云中路由预测与识别的难度,影响云计算效率。针对以上问题,提出一种基于自适应免疫分组多态蚁群算法的云数据库动态路径优化过程。通过设置侦察蚁和搜索蚁两种状态蚁群,并引入自适应多态蚁群竞争策略,改善一般蚁群算法易陷入局部最优解的缺陷;在此基础上进一步融合具有快速全局搜索能力的人工免疫算法对搜索蚁路径优化过程进行改进,提高搜索速度和精度。仿真实验表明,该算法能更好地解决收敛速度和全局最优问题,能够在云中快速、合理地找到所需访问的数据库。Distribution,dynamicity of the cloud database increase the difficulty of the routing prediction and identification,and also affect the efficiency of the clouds. To solve above problem,this paper proposed a kind of cloud database dynamic path optimization process based on immune polymorphic ant colony algorithm. It set the scout ants and search ants,and introduced the adaptive polymorphic ant colony competition strategy,improved the defect of easy to fall into local optimal solution of general ant colony algorithm; and on the basis,used the artificial immune algorithm with rapid global search ability to improved the search ants path optimization process,increased the search speed and precision. Simulation experiments show that this algorithm can better solve the problem of convergence speed and global optimum,quickly and reasonably find the database what they need to access.

关 键 词：自适应多态蚁群竞争策略 免疫多态蚁群算法 云数据库 动态路径优化 

分 类 号：TP301.5[自动化与计算机技术—计算机系统结构]