耦合优化蚁群算法与P-Median model的选址模型设计

Design of location model based on coupled optimization ant colony algorithm and P⁃Median model

为节省城建部门对于公共体育设施的投入成本以及提高城市人民生活质量,以运动场所优化选址为例,提出一种新型设施选址模型。该模型主要基于P-Median model(最小化阻抗模型)根据需求点数量从全部候选设施选址中选择设施空间位置,让用户达到离自己最近设施距离成本总和最小的目的,对选址的基本原则和实际情况提出要求,构造目标函数用于优化后蚁群算法求解进行选址工作。优化蚁群算法实现基于Python语言模块,通过改进蚁群原始信息素,提升原有算法的收敛速度,求出目标函数最优解,可以很好地模拟对于运动场所的选址。用二者耦合进行优势互补所设计的选址模型来搜寻研究区蚁群信息素浓度残留最大的栅格像元,从而确定未被已有设施点服务半径覆盖的最佳设施点建立位置。实验结果表明,该新型选址模型相较于最小化阻抗模型与最大化覆盖模型,新增优化设施点使整体服务半径覆盖率分别高出10.42%和6.95%,适合求解较为精确且小规模空间下的选址问题。

A new facility location model is proposed by taking the optimal location of sports venues as an example,so as to save the investment cost of urban construction departments for public sports facilities and improve the quality of life of urban people.The model is mainly based on the P⁃Median model(minimum impedance model)to select the spatial location of the facility from all candidate facility locations according to the number of demand points,so that the user can achieve the purpose of minimizing the sum of the cost of the distance from the nearest facility.The basic principles and actual situation of the location are required,and the objective function is constructed for the optimized ant colony algorithm to solve the location work.The optimized ant colony algorithm is based on the Python language module.By improving the original pheromone of the ant colony,the convergence speed of the original algorithm is improved,and the optimal solution of the objective function is obtained,which can well simulate the location of the sports place.The location model designed by coupling the two to complement each other′s advantages is used to search for the grid pixels with the largest residual pheromone concentration in the study area,so as to determine the optimal location of the facility points that are not covered by the service radius of the existing facilities.The experimental results show that the new optimization facility point increases the overall service radius coverage by 10.42%and 6.95%respectively in comparison with the minimum impedance model and the maximum coverage model,which is suitable for solving more accurate and small⁃scale location problems.