地理学报

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基于耦合的地理模拟优化系统

黎夏, 刘小平, 何晋强, 李丹, 陈逸敏, 庞瑶, 李少英   

  1. 中山大学地理科学与规划学院
  • 收稿日期:2009-03-12 修回日期:2009-04-30 出版日期:2009-08-20 发布日期:2009-09-21
  • 作者简介:黎夏, 男, 教授, 博士生导师, 中国地理学会会员(S110001500m), 主要从事城市模拟与优化研究, 已发表GIS和遥感论文200多篇。E-mail: lixia@mail.sysu.edu.cn
  • 基金资助:

    国家自然科学基金重点项目(40830532);; 国家杰出青年基金项目(40525002);; 国家高技术研究发展计划(863计划)(2006AA12Z206)

A Geographical Simulation and Optimization System Based on Coupling Strategies

LI Xia,  LIU Xiaoping,  HE Jingqiang,  LI Dan,  CHEN Yimin,  PANG Yao,  LI Shaoying   

  1. School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
  • Received:2009-03-12 Revised:2009-04-30 Online:2009-08-20 Published:2009-09-21
  • Supported by:

    The Key Project of National Natural Science Foundation of China (No.40830532); National Outstanding Youth Foundation of NSF of China, No.40525002; Hi-Tech Research and Development Program of China, No.2006AA12Z206

摘要:

尽管GIS在涉及空间信息的许多学科和行业有广泛的应用,但其在对过程进行模拟和优化方面存在严重的功能不足。本文提出地理模拟优化系统GeoSOS的概念与实现方法。进一步建立了GeoSOS1.0的模拟优化平台,作为GIS的重要补充工具。包含了三个重要部分:地理元胞自动机(CA)、多智能体系统(MAS)、生物智能(SI)。其核心内容就是根据微观个体的相互作用,达到模拟和优化的目的。根据Tobler地理学的第一定律,提出了GeoSOS的统一的相互作用规则。GeoSOS具备将模拟和优化耦合起来的功能。将动态模拟模型与空间优化模型耦合起来,使得优化的方案具有一定的前瞻性。对比实验结果发现,耦合模型产生的效用值比非耦合模型分别高出4.3%(点状优化)和4.1%(线状优化),表明GeoSOS能够改善优化的结果。

关键词: 地理模拟优化系统, 地理元胞自动机, 多智能体系统, 生物智能, 耦合

Abstract:

Geographic Information Systems (GIS) have been widely used for research purposes in numerous disciplines. The solution to the increasingly intensified resource and environmental problems requires sophisticated simulation and optimization tools. Geographers need to deal with more data and more complex models for analyzing geographical processes. GIS have a good capability of handling spatial data, but have limitations of performing complex simulation and optimization tasks. This paper first discusses the concepts and methodologies of a Geographical Simulation and Optimization System (GeoSOS). GeoSOS 1.0 is further developed to provide advanced toolboxes for implementing a series of simulation and optimization tasks. As a bottom-up approach, GeoSOS 1.0 consists of three major integrated components, cellular automata (CA), multi-agent systems (MAS), and swarm intelligence (SI). The binding force of this system is the interactions between spatial micro-entities and their environment. The interactions are governed by Tobler's first law of geography. A general form of interaction rules is proposed for the synergy of these three bottom-up components. A set of data mining tools can be used to discover the interaction rules of GeoSOS. The integration of CA with MAS can allow the system to handle various kinds of simulation tasks. Another novelty of this proposed system is its capability of coupling the simulation (CA and MAS) with the optimization (SI). The scenario with the highest accumulative utility value can be identified by using this coupling mechanism. This proposed system provides a new kind of functionality to improve the understanding of natural complex systems.

Key words: geographical simulation and optimization systems, geographical cellular automata, multi-agent systems, swarm intelligence, coupling