CAJ | 학술논문

蚁群算法对于解决动态最优路径查询问题具有很强的优势,但蚁群算法中的信息素挥发因子的静态设置容易带来收敛速度不稳定和陷入局部最优解的问题,在云数据库中更是明显.融合了蚁群算法和云数据库,并提出了信息素挥发因子自适应的算法,该算法能够在云中快速、合理地找到所需访问的数据库,减少了云数据库数路由的动态负荷,从而很大程度上提高云计算的效率.
의군산법대우해결동태최우로경사순문제구유흔강적우세,단의군산법중적신식소휘발인자적정태설치용역대래수렴속도불은정화함입국부최우해적문제,재운수거고중경시명현.융합료의군산법화운수거고,병제출료신식소휘발인자자괄응적산법,해산법능구재운중쾌속、합리지조도소수방문적수거고,감소료운수거고수로유적동태부하,종이흔대정도상제고운계산적효솔.
Although ACO(Ant Colony Optimization) algorithm has strong advantages in treating dynamical optimal route query problem,pheromone volatility factor's static setting brings unstable convergence speed and traps into local optimization answer problems,especially for cloud database.Combining ACO algorithm and cloud database,this paper proposes a novel pheromone volatility self-adaptive algorithm which can find the requiring database rapidly and effectively,and reduce the dynamic routing burdens of cloud database routing,and enhance the efficiency of cloud computing to a large extent.