蚁群智能及其在大区域基础设施选址中的应用  

Ant colony algorithms for optimal site selection in large regions

作　　者：何晋强[1] 黎夏[1] 刘小平[1] 陶嘉[1] 

机构地区：[1]中山大学地理科学与规划学院,广东广州510275

出　　处：《遥感学报》2009年第2期246-256,共11页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家杰出青年基金资助项目(编号:40525002);国家自然科学基金重点资助项目(编号:40830532);国家863计划资助项目(编号:2006AA12Z206)

摘　　要：提出了基于蚁群智能的空间选址模型,通过蚁群智能和GIS的结合来解决复杂的空间优化配置问题。这种启发式的智能搜索方法大大提高了空间搜索能力。为符合选址问题的求解,从信息素更新方式和禁忌表调整策略两方面对基本蚁群算法进行改进。同时,为了使得模型能实用于大区域的基础设施选址,提出了"分步逼近"的策略,取得了较好的效果。将所提出的模型应用于广州市公共设施的空间优化选址。实验结果表明,该方法比简单搜索方法和遗传算法更有优势。Optimal site search for sitting facilities is crucial for the effective use and management of resources and it is also a common task for urban planning. The brute-force method has difficulty in solving complex site search problems especially in large scale areas. In this study, a location model is proposed based on ant colony algorithms （ ACO）. It combines ant colony intelligence and GIS to solve the problems of complicated spatial optimal allocation. ACO has strong search ability for a huge volume of spatial data. At first, the algorithm is modified about the strategy of pheromone update and Tabu table adjusting to fit the sites location problem. Spatial allocation problems usually have a large set of spatial data and only a few targets. The pheromone evaporates very fast because the selected cells only amount to a small percentage of all the ceils. The positive feedback is too weak to play a role in the optimization. A modification is to incorporate the strategy of neighborhood pheromone diffusion in defining pheromone updating. At the same time, an optimal result for sites selection usually does not include two near candidate cells together, so a restricted Tabu table updating strategy is adopted which resembles the strategy of neighborhood pheromone diffusion. Then another important modification is to adopt a multi-scale approach to alleviate the computational demand in conducting large-scale spatial search. This includes two phases of optimization. First, a coarser resolution is used for the identification of rough locations of targets using ACO. Then the next round of optimization is implemented by just searching the neighborhood around these initial locations in the original resolution. This two-phase procedure of optimization can thus significantly reduce the computation time. The study area is located in the city of Guangzhou. This optimization problem considers two spatial variables, population distribution and transportation conditions, which are obtained from GIS. The raster layers have a re

关 键 词：蚁群算法 GIS 选址 简单搜索算法 遗传算法 

分 类 号：P208[天文地球—地图制图学与地理信息工程] TP18[天文地球—测绘科学与技术]