道路网匹配的蚁群算法求解模型

巩现勇; 武芳; 姬存伟; 翟仁健

doi:10.13203/j.whugis20120649

道路网匹配的蚁群算法求解模型

¹ 信息工程大学地理空间信息学院,河南郑州,450052;² 地理信息工程国家重点实验室,陕西西安,710054

基金项目: 国家自然科学基金资助项目 (41171354,41101362,41171305)；地理信息工程国家重点实验室开放研究基金资助项目(SKLGIE2013-M-4-6)

详细信息

作者简介:
巩现勇,硕士生,研究方向为模式识别、自动制图综合和时空数据分析。

中图分类号: P208;P283.1
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出版历程
- 收稿日期: 2013-05-28
- 修回日期: 2014-02-04
- 发布日期: 2014-02-04

Ant Colony Optimization Approach to Road Network Matching

¹Institute of Surveying and Mapping,Information Engineering University,Zhengzhou 450052,China;²State Key Laboratory of Geo-information Engineering,Xi’an 710054,China

Funds: The National Natural Science Foundation of China,Nos.41171354,41101362,41171305;the State Key Laboratoryof Geo-information Engineering Foundation,No.SKLGIE2013-M-4-6.

More Information

Author Bio:
GONG Xianyong,postgraduate,specializes in pattern recognition,automated cartography generalization and spatio-temporaldata analysis.

摘要

摘要: 目的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。
- 数据更新 /
- 同名实体匹配 /
- 蚁群算法 /
- 组合优化
Abstract: Objective Corresponding feature matching,essentially as a matter of global combinatorial optimiza-tion,is one of the key technologies for geospatial data integration,fusion and update.In this paper,aglobal optimum matching solution is achieved taking the advantages of ant colony optimization groupsand random search,without the centralized control and global model.The basic principle of ant colonyoptimization for road network matching is explained first,with a mathematical constraint model con-sidering both geometric error and structural characteristics.Then,the matching problem solutionmodel 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.
- spatial data update /
- corresponding feature matching /
- ant colony optimization /
- combinato-rial optimization