基于蚁群算法的城市体育设施优化选址

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。

Optimal Location of Urban Sports Facilities Based on Ant Colony Algorithm

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.