地理学报 ›› 2004, Vol. 59 ›› Issue (5): 745-753.doi: 10.11821/xb200405013

• 区域发展分析 • 上一篇    下一篇

遗传算法和GIS结合进行空间优化决策

黎夏1, 叶嘉安2   

  1. 1. 中山大学地理科学与规划学院, 广州510275;
    2. 香港大学城市规划及环境管理研究中心, 香港
  • 收稿日期:2004-01-10 修回日期:2004-06-03 出版日期:2004-09-25 发布日期:2010-09-09
  • 作者简介:黎夏 (1962-), 男, 教授, 博士生导师。1983年硕士毕业于北京大学, 1996年获香港大学博士学位, 1997-1998年在香港大学进行博士后研究。主要从事遥感和地理信息系统研究。在国内外刊物上发表近100篇学术论文。E-mail: gplx@zsu.edu.cn
  • 基金资助:

    国家自然科学基金项目 (批准号 40071060)

Optimal Spatial Search Using Genetic Algorithms and GIS

Xia LI1, Anthony Gar-On YEH2   

  1. 1. School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China;
    2. Centre of Urban Planning and Environmental Management, The University of Hong Kong, Hong Kong, China
  • Received:2004-01-10 Revised:2004-06-03 Online:2004-09-25 Published:2010-09-09
  • Supported by:

    National Natural Science Foundation of China, No. 40071060

摘要:

资源的有效利用和管理往往涉及到空间的优化配置问题。例如需要在空间上确定n个设施的最佳位置。当选址问题涉及多个目标和不同的约束性条件时,就会变得十分复杂。利用一般的brute-force搜索方法无法对涉及高维数据的问题进行求解。利用遗传算法和GIS结合来解决复杂的空间优化配置问题,具有智能的搜索方法可以大大提高空间的搜索能力。在基于进化的优化过程中,根据GIS的空间数据来计算不同解决方案 (染色体) 的适应度。针对不同的应用目的,GIS可以给出不同的适应度函数。实验表明,所提出的方法比简单的搜索方法和退火算法有更大的优越性。该方法在处理复杂的空间优化问题有更好的表现。

关键词: 遗传算法, GIS, 空间优化, 退火算法

Abstract:

This study demonstrates that genetic algorithms are capable of producing satisfying results for optimal spatial search under complex situations. We successfully solve a spatial search problem using the proposed method to allocate the facility according to the population constraint from GIS. The search algorithm is very simple using the mechanics of natural selection in biology. The proposed method can be used as a planning tool that can help urban planners to improve development efficiency in site selection. The method is developed by a common computer language which can directly use the full functions of a commercial GA package through the DLL and can import the spatial data from GIS. This integration is useful for solving realistic problems by using large spatial data sets. The programming can be easily adapted to other applications by just modifying the fitness functions instead of changing the model itself. The proposed method has been tested in the city of Hong Kong, a densely populated region. The population data are obtained from the census department and the population density is prepared in GIS as the main inputs to the GA programming.

Key words: genetic algorithms, GIS, spatial optimization, simulated annealing