多叉树蚁群算法及在区位选址中的应用研究

本文提出了基于多叉树蚁群算法(ant colony optimization based on multi-way tree) 的区 位选址优化方法。在多目标和大型空间尺度约束条件下，地理区位选址的解决方案组合呈现 海量规模、空间搜索量庞大，难以求出理想解。基于多叉树的蚁群算法对地理空间进行多叉树划分，在多叉树的层上构造蚂蚁路径(ant path)，让蚂蚁在多叉树的搜索路径上逐步留下信息 素，借助信息素的通讯来间接协作获得理想的候选解。采用该方法用于广州市的地理区位选址，取得良好结果。实验结果表明：采用基于多叉树的蚁群算法，改善了蚂蚁在空间搜索能 力，适合求解大规模空间下的区位选址问题。

An Ant Colony Algorithm Based on Multi-way Tree for Optimal Site Location

Site location by brute-force method is difficult for optimization due to massive spatial data and huge solution space under the constraint condition of multi-objective and large spatial resolutions.In this study,an improved ant colony optimization(ACO) based on multi-way tree is introduced to solve site location problem.Better solutions can be obtained swiftly according to the density of pheromone the ants leave on the search paths constructed in nested subspaces divided by means of the multi-way tree algor...