| Discovery of transition rules for geographical cellular automata by using ant colony optimization |
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| 引用本文： | Anthony Gar-On YEH. Discovery of transition rules for geographical cellular automata by using ant colony optimization[J]. 中国科学D辑(英文版), 2007, 50(10): 1578-1588. DOI: 10.1007/s11430-007-0083-z |
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| 作者姓名： | Anthony Gar-On YEH |
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| 作者单位： | LIU XiaoPing（School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China） ;LI Xia（School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China） ;Anthony Gar-On YEH（Center of Urban Planning and Environmental Management, The University of Hong Kong, Hong Kong SAR, China） ;HE JinQiang（School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China） ;TAO Jia（School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China） ; |
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| 基金项目： | 国家杰出青年科学基金;国家高技术研究发展计划(863计划);国家自然科学基金;面向21世纪教育振兴行动计划(985计划) |
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| 摘 要： | A new intelligent algorithm of geographical cellular automata (CA) based on ant colony optimization (ACO) is proposed in this paper. CA is capable of simulating the evolution of complex geographical phenomena, and the core of CA models is how to define transition rules. However, most of the transition rules are defined by mathematical equations, and are hence not explicit. When the study area is complicated, it is much more difficult to extract parameters for geographical CA. As a result, ACO is applied to geographical CA to automatically and intelligently obtain transition rules in this paper. The transition rules extracted by ACO are defined as logical expressions rather than implicit mathematical equations to describe the complex relationships of the nature, and easy for people to understand. The ACO-CA model was applied to simulating rural-urban land conversions in Guangzhou City, China, and appropriate simulation results were generated. Compared with See5.0 decision tree model, ACO-CA is more suitable to discovering transition rules for geographical CA.
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| 收稿时间： | 2006-11-13 |
| 修稿时间： | 2007-02-13 |
Discovery of transition rules for geographical cellular automata by using ant colony optimization |
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| Liu XiaoPing,Li Xia,Anthony Gar-On Yeh,He JinQiang,Tao Jia. Discovery of transition rules for geographical cellular automata by using ant colony optimization[J]. Science in China(Earth Sciences), 2007, 50(10): 1578-1588. DOI: 10.1007/s11430-007-0083-z |
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| Authors: | Liu XiaoPing Li Xia Anthony Gar-On Yeh He JinQiang Tao Jia |
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| Affiliation: | 1. School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
2. Center of Urban Planning and Environmental Management, The University of Hong Kong, Hong Kong SAR, China |
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| Abstract: | A new intelligent algorithm of geographical cellular automata (CA) based on ant colony optimization (ACO) is proposed in this paper. CA is capable of simulating the evolution of complex geographical phenomena, and the core of CA models is how to define transition rules. However, most of the transition rules are defined by mathematical equations, and are hence not explicit. When the study area is complicated, it is much more difficult to extract parameters for geographical CA. As a result, ACO is applied to geographical CA to automatically and intelligently obtain transition rules in this paper. The transition rules extracted by ACO are defined as logical expressions rather than implicit mathematical equations to describe the complex relationships of the nature, and easy for people to understand. The ACO-CA model was applied to simulating rural-urban land conversions in Guangzhou City, China, and appropriate simulation results were generated. Compared with See5.0 decision tree model, ACO-CA is more suitable to discovering transition rules for geographical CA. |
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| Keywords: | ant colony optimization CA geographical simulation artificial intelligence |
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