基于蚁群算法多层次动态信息提取仿真研究

研究动态信息偏好捕捉精确度问题。网络数据存在重复性信息和随机性强。针对互联网中的大量数据,而造成了有效的信息的查找速度慢等缺陷,为了能够快速的获取更多的用户比较感兴趣信息,提出了一种改进的蚁群算法用户兴趣模式获取技术。面向层次结构的信息网站,算法首先根据网站和用户兴趣所具有的层次性特征,然后采用改进的蚁群算法较高的寻优机制,利用蚂蚁的觅食周期活动,从各个层次求出相应路径的信息素浓度,并适时的实行信息素更新机制,从而得到用户对该结点的偏好函数值,再依据此值求得用户兴趣模式。仿真结果表明,提出的方法能够有效地捕捉出用户兴趣信息,捕捉精确度较高,是一种有效的方法,具有一定的推广价值。

Interest Management Based on Ant Colony Algorithm

The research dynamic interest preference captures accuracy problems.For large data in the network structure caused by defects such as slow searching information,in order to be able to retrieve the user interest of more information,and proposes an improved ant colony algorithm user interests mode acquisition algorithm was presented for hierarchical structure,this paper firstly information website,according to the website and user interests have gradation,and then modified characteristics of ant colony algorithm higher searching mechanism,using the ants foraging cycle from all levels for activities,the corresponding path pheromone strength,and timely implement pheromone update mechanism,thus obtains the user to the node preferences function values,again according to the user’s interests mode.For value Simulation experiments show that the proposed method can effectively capture a user interests information,capture more accurate,is a kind of effective method.