Abstract: This paper demonstrates that using particle swarm optimization approach solves optimal location of supermarkets based on GIS. First, the paper analyzes the factors of affecting the work of supermarkets, which include population density, traffic and competition. Second, the paper elaborates on the implementing procedure and method of optimal location of supermarkets by using PSO and GIS under population, traffic and competition constraint conditions. Finally, the paper verifies this method by a case of Fangcun District, Guangzhou. It is concluded that particle swarm optimization is a robust method of solving spatial optimal search under complex condition.

Key words: Particle Swarm Optimization, Location of Supermarkets, GIS

摘要： 本文尝试使用微粒群优化算法与GIS相结合解决超市最优选址问题。首先，本文对影响超市经营好坏的因子进行了分析，包括：人口密度、交通因子以及竞争因子的影响；然后，详细阐述了微粒群优化算法与GIS技术相结合用于解决超市最优选址的实施方法；最后，以广州市芳村区为例，对PSO方法进行实例验证。通过与穷举法进行对比实验，证明微粒群优化算法具有较好的收敛速度、较高的结果精度，是解决超市最优选址的一种有效方法。

关键词: 微粒群优化算法 最优选址 GIS