基于三阶段蚁群算法的土地利用核查 路径规划与目标导航

针对当前土地利用监管外业核查难以快速、准确地遍历所有待核查地块等问题,进行了土地核查路径规划与目标导航定位技术研究。提出了基于改进蚁群算法的土地核查路径规划与目标导航问题的三阶段求解方法,即“先分群,再搜索阶段最优路径,最后实现全局最优路径规划”,将大区域的多辆车路径规划问题简化为小范围单辆车路径规划与目标导航,利用改进蚁群算法求解出土地核查全局最优路径和导航信息。在此基础上对扬州市面积约6 600 km2范围内580个待核查图斑开展土地核查,利用该算法将外业核查车辆行驶路程由2 250 km缩短为1 683.3 km,缩短了25.2%。精准的目标导航方法较采用商用导航仪提高了工作效率和核查目标导航的准确性。
In view of the problem that it was difficult for land management staffs to fast and accurately find and reach all land parcels need to be inspected in land use inspection work, the technique of land use inspection routing plan and object navigation was studied. A three-stage ant colony algorithm for land inspection routing plan and target navigation was presented, i.e. “first, clustering targets, then searching local optimal path, at last realizing global optimum path”. The three-stage ant colony algorithm simplified multiple vehicle routing and targets navigation in a large area to sole vehicle routing and targets navigation in a small area, and searched the land supervision global optimal routing and navigation information based on improved ant colony algorithm. The experiment showed that, through surveying 580 pattern spots in 6 600 km2, the method improved vehicle-miles of travel from 2 250 km to 1 683.3 km, shorten by 25.2%. At the same time this accurate object navigation enhanced work efficiency and veracity if compared to exacting commercial navigator method