道路网匹配的蚁群算法求解模型

Ant Colony Optimization Approach to Road Network Matching

利用蚁群算法的群体优势,寻找全局最优的道路网同名实体匹配方案。首先从几何矢量误差和结构特征两方面建立了匹配问题的数学约束模型;然后阐述了蚁群算法求解匹配问题的基本原理,设计了问题求解模型,并引入自适应和局部搜索策略提高了算法效率;最后给出了求解的关键步骤。实验证明,利用蚁群算法进行道路网匹配是有效、可行的,为求解匹配问题提供了新思路。

Corresponding feature matching, essentially as a matter of global combinatorial optimization, is one of the key technologies for geospatial data integration, fusion and update. In this paper, a global optimum matching solution is achieved taking the advantages of ant colony optimization groups and random search, without the centralized control and global model. The basic principle of ant colony optimization for road network matching is explained first, with a mathematical constraint model con- sidering both geometric error and structural characteristics. Then, the matching problem solution model is designed, with a self-adaptation and local search strategy employed to improve efficiency. Fi- nally, the key steps are given. Experiments show that the ant colony optimization approach is effec- tive, feasible and practical, providing a new idea for road network matching.