本文提出了基于遗传算法来寻找CA模型最佳参数的方法。CA被越来越多地应用于城市和土地利用等复杂系统的动态模拟。CA模型中变量的参数值对模拟结果有非常重要的影响。如何获取理想的参数值是模型的关键。传统的逻辑回归模型运算简单,常常用来获取模型的参数值,要求解释变量间线性无关,所以获取的城市CA模型参数具有一定的局限性。遗传算法在参数优化组合、快速搜索参数值方面有很大的优势。本文利用遗传算法来自动获取优化的CA模型参数值,并获得了纠正后的CA模型。将该模型应用于东莞1988~2004年的城市发展的模拟中,得到了较好的效果。研究结果表明,遗传算法可以有效地自动获取CA模型的参数,其模拟的结果要比传统的逻辑回归校正的CA模型模拟精度高。
This paper presents a new method to calibrate urban cellular automata (CA) using genetic algorithms(GA).The GA is used to find the optimal parameter values so that CA models can simulate urban expansion in a more realistic way. Traditional multi-criterion evaluation (MCE) and logistic methods have limitations for deriving the transition rules of CA models. The variables should be independent so that the parameter values (coefficients) can be properly estimated by regression analysis. This assumption is not true in most situations. The limitations can be overcome by using GA to estimate these parameter values for these correlated variables. When calibrating urban cellular automata with GA, the parameters of CA models are set to the chromosomes in GA program.The real number encoding way is used to encode chromosomes. The fitness function is defined with mean square error between simulated and actual urban forms.The initial population is set to be 50 randomly. And crossover probability is set to be 0.9, and mutation probability is set to be 0.01. The elitist selection is used to heredity the better individual.If the fitness does not change in the past 50 generations, the genetic procedure will be finished. After properly encoding the chromosomes, the optimal parameter values are automatically found by the evolutionary approach. This method is applied to the simulation of urban expansion in Dongguan, a fast developing city in the Pearl River Delta in South China. The model is able to simulate urban development in 1988-2004 by using the training data from remote sensing data. The analysis indicates that the proposed model can produce better simulation results than MCE-based CA models and logistic calibrated CA models. Moreover, the parameter values can be used to explain the relationships between spatial variables and urban development.
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