基于改进人工势场-鱼群算法的LBS最短路径修正研究

ON LBS SHORTEST PATH CORRECTION BASED ON IMPROVED ARTIFICIAL FISH SWARM ALGORITHM WITH POTENTIAL FIELD

LBS(Location Based Service),即基于位置的服务,是通过无线通信设施或是外部定位方式取得移动终端用户位置信息,在GIS系统的支持下,为用户提供相应位置服务的一种增值服务。以LBS系统中动态目标的追踪为研究对象,通过对基础人工势场法进行研究,提出一种改进人工势场法:在引力场中加入速度因素,使其能追踪动态目标。同时,由于复杂环境的多变性,将改进人工势场法与人工鱼群算法相结合,对路径进行评估和修正,获得最优路径。仿真实验显示,该算法在LBS系统中的有效性,同时与一般智能算法相比,基于改进人工势场-鱼群算法到达目标时间明显缩短,提高了搜索效率。

LBS （location-based service）is a value-added service providing users the related location services with the support of GIS system by acquiring the location information of mobile terminal users through wireless communication facilities or external positioning means. In this paper we take tracking the dynamic target in LBS system as the research object and put forward an improved artificial potential field method through studying the basic artificial potential field method：by adding the speed factor in the gravitational field to enable it tracking the dynamic target.Meanwhile,due to the variability of complex environment,we combine the improved artificial potential field method with artificial fish swarm algorithm to evaluate and correct the path to obtain the optimal path.It is demonstrated through simulation experiment the effectiveness of the proposed algorithm in LBS system.Compared with general intelligent algorithms,the target approaching time by the algorithm based on improved artificial fish swarm with potential field shortens significantly,this proves that the new algorithm improves the searching efficiency.