基于网络Voronoi图和多目标微粒群的空间选址优化

A Technical Method for Spatial Location Optimization Based on Network Voronoi Diagram and Multi-objective Particle Swarm

根据网络Voronoi图对中心设施影响范围的空间划分和多目标微粒群的智能搜索提出了一种空间选址的技术方法。城市功能设施的选址往往受到交通网络的影响,而且涉及多个优化目标和约束条件,利用最短路径分析构建的网络Voronoi图来模拟设施的辐射影响范围,并根据其他优化目标和约束条件,使用经过粒子记忆体和遗传交叉机制改进的多目标微粒群算法智能地搜索设施的空间布局位置。实验结果表明,该方法能够较好地模拟出在交通网络和多个约束条件影响下的有限个功能设施的较优布局方案,具有一定的参考价值。

A technical method for spatial location selection based on network Voronoi diagram spatial dividing and multi-objective particle swarm intelligent search is proposed in this paper.The spatial location selection for service facilities in urbanized area is usually influenced by transportation network and involves one or more optimization goals and constraint conditions,this paper uses network Voronoi diagram to simulate the influence area of service facility by shortest path analysis and solves the spatial location selection problem of service facilities by multi-objective particle swarm algorithm which is improved by extended individual memory and genetic crossover mechanism with other optimization goals and constraint conditions.Choosing an area of Washington DC as experimental area,an experiment is applied for verification,which shows the method can get preferable location selection on the condition of transportation network and other constraints and have some reference value.