CAJ | 학술논문

尽管G1S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GcoSOS 1.0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.
진관G1S재섭급공간신식적허다학과화행업유엄범적응용,단기재대과정진행모의화우화방면존재엄중적공능불족.본문제출지리모의우화계통GeoSOS적개념여실현방법.진일보건립료GcoSOS 1.0적모의우화평태,작위GIS적중요보충공구.포함료삼개중요부분:지리원포자동궤(CA)、다지능체계통(MAS)、생물지능(SI).기핵심내용취시근거미관개체적상호작용,체도모의화우화적목적.근거Tobler지이학적제일정률,제출료GeoSOS적통일적상호작용규칙.GeoSOS구비장모의화우화우합기래적공능.장동태모의모형여공간우화모형우합기래,사득우화적방안구유일정적전첨성.대비실험결과발현,우합모형산생적효용치비비우합모형분별고출4.3%(점상우화)화4.1%(선상우화),표명GeoSOS능구개선우화적결과.
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.