Discovery of transition rules for geographical cellular automata by using ant colony optimization

作     者：Anthony Gar-On YEH 

作者机构：Center of Urban Planning and Environmental Management The University of Hong KongHong Kong SAR China 

出 版 物：《Science China Earth Sciences》 (中国科学（地球科学英文版）)

年 卷 期：2007年第50卷第10期

页      面：1578-1588页

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 070105[理学-运筹学与控制论] 0701[理学-数学] 

基　　金：National Outstanding Youth Foundation of China (Grant No. 40525002) the National 863 Project of China (2006AA12Z206) the National Natu-ral Science Foundation of China (Grant No. 40471105) the "985 Project" of GIS and Remote Sensing for Geosciences from the Ministry of Education of China (Grant No. 105203200400006) 

主　　题：ant colony optimization, CA, geographical simulation, artificial intelligence 

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.