期刊
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
卷 22, 期 11-12, 页码 1247-1269出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/13658810701757510
关键词
Ant colony optimization; CA; Urban simulation; Artificial intelligence
类别
资金
- National Outstanding Youth Foundation of China [40525002]
- National Natural Science Foundation of China [40471105]
- Hi-tech Research and Development Program of China [2006AA12Z206]
This paper presents a new method to discover transition rules of geographical cellular automata (CA) based on a bottom-tip approach, ant colony optimization (ACO). CA are capable of simulating the evolution of complex geographical phenomena. The core of a CA model is how to define transition rules so that realistic patterns can be simulated using empirical data. Transition rules are often defined by using mathematical equations, which do not provide easily understandable explicit forms. Furthermore, it is very difficult, if not impossible, to specify equation-based transition rules for reflecting complex geographical processes. This paper presents a method of using ant intelligence to discover explicit transition rules of urban CA to overcome these limitations. This 'bottom-up' ACO approach for achieving complex task through cooperation and interaction of ants is effective for capturing complex relationships between spatial variables and urban dynamics. A discretization technique is proposed to deal with continuous spatial variables for discovering transition rules hidden in large datasets. The ACO-CA model has been used to simulate rural-urban land conversions in Guangzhou, Guangdong, China. Preliminary results suggest that this ACO-CA method can have a better performance than the decision-tree CA method.
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