人工免疫系统与嵌入规划目标的城市模拟及应用

人工免疫系统(AIS) 具有强有力的计算能力, 可以通过免疫识别、克隆选择、免疫学 习、免疫记忆等功能来进行模式识别和自适应学习。AIS 所具有的自学习、自适应和记忆的能力非常适合于复杂地理过程的研究。而元胞自动机(CA) 是研究复杂系统非常方便和有效的工具。将人工免疫系统和元胞自动机相结合, 建立了城市演变的模拟和规划模型。该模型通过改变抗体的进化变异机制, 把规划目标嵌入到AIS 算法中, 抗体将会逐渐朝着规划目标“进化”, 从而模拟出基于不同规划情景的城市发展空间格局, 为城市和土地利用规划提供决 策支持。设计了6 种不同的城市发展方案, 利用该模型模拟了不同规划方案下珠江三角洲城市的发展情景(1988-2002 年)。并比较了不同模拟情景结果城市的紧凑性: “城市中心” 和 “城市中心-高速公路”发展模式的城市形态更为紧凑, 破碎度较低; 而“镇中心” 和“道路”发展模式形成的城市形态则比较凌乱和分散。模拟结果和分析表明: “城市中心-高速 公路”是珠江三角洲最适合的城市发展模式。

Embedding Urban Planning Objective by Integrated Artificial Immune System and Cellular Automata

Artificial Immune System can be used in pattern recognition and self-adaptive learning for its strong computing power such as immune recognition, clonal selection, immune learning and immune memory, which is quite suitable for studying the complex geographical progress. And CA is proved to be convenient and effective for studying complex system. As a result, model based on integrating AIS with CA was built to simulate the urban evolution and planning in this paper. As planning objective was embedded into AIS algorithm, antibody will gradually evolve towards which by changing the evolutionary variation mechanism. Then urban developing spatial pattern based on different planning scenarios can be simulated, which will supply decision support for urban and land use planning. This paper designed six different scenarios for city development, and used AIS-based CA model to simulate the Pearl River Delta's urban development (1988-2002) under different planning scenarios. It also compared the urban compactness under different simulation results: "City Center" and "City Center-Expressway" models incline to result in a more compact form of urban; On the other hand, "Town Center" and "Road" models come into being a relatively scattered and decentralized form of urban areas. Simulated results indicate that "City Center-Expressway" is the best development mode for the Pearl River Delta.