Acta Geographica Sinica

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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

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