基于蚁群算法的城市体育设施优化选址  

Optimal Location of Urban Sports Facilities Based on Ant Colony Algorithm

作　　者：李显良 周庆平 谭长庚[3] 谭焱良 徐则阳 LI Xian-liang;ZHOU Qing-ping;TAN Chang-geng;TAN Yan-liang;XU Ze-yang(School of Science,Central South University of Forestry Science and Technology,Changsha 410004,China;Dept.of Scientific Research,Hunan Sports Vocational College,Changsha 410019,China;School of Computer Science,Central South University,Changsha 410075,China)

机构地区：[1]中南林业科技大学理学院,湖南长沙410004 [2]湖南体育职业学院科研处,湖南长沙410019 [3]中南大学计算机学院,湖南长沙410075

出　　处：《计算机与现代化》2020年第3期33-39,共7页Computer and Modernization

基　　金：国家自然科学基金资助项目(61672540,61379057);湖南省科技计划资助项目(2015SK2056);湖南省教育厅科学研究项目(18C1521)。

摘　　要：在多目标以及大型空间约束情况下的城市体育设施选址求解规模较大,难以求解出理想的解集。本文提出一种改进的蚁群智能算法模型,模型主要通过改进蚁群原始信息素分布以及挥发系数,加快算法的收敛速度以及精度,得出理想的候选解。将该方法应用于长沙市雨花区的体育设施选址,取得了较好的效果,实验结果表明,采用本文所设计的改进蚁群算法模型,适合求解大规模空间下的城市体育设施选址问题。In the case of multi-objective and large-scale space constraints,the problem scale of the location of urban sports facilities is large,and it is difficult to obtain the ideal solution set.An improved ant colony intelligent algorithm model is proposed.The model mainly improves the convergence speed and accuracy of the algorithm by improving the original pheromone distribution and the evaporation coefficient of the ant colony,and calculates the ideal candidate solution.The method is applied in the site selection of sports facilities in Yuhua district of Changsha city,which gets good results.The experimental results show that the improved ant colony algorithm model designed in this paper is suitable for solving the problem of urban sports facilities location in large-scale space.

关 键 词：蚁群算法 体育设施 选址优化 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]