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首页> 外文期刊>Computers，environment and urban systems >Comparison of metaheuristic cellular automata models: A case study of dynamic land use simulation in the Yangtze River Delta
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Comparison of metaheuristic cellular automata models: A case study of dynamic land use simulation in the Yangtze River Delta

机译：元启发式元胞自动机模型的比较：以长三角地区动态土地利用模拟为例

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摘要

Cellular automata (CA) is a bottom-up modeling framework that has increasingly been applied to simulate land use change by capturing its dynamics. Metaheuristics such as particle swarm optimization (PSO), generalized simulated annealing (GSA) and genetic algorithm (GA) have widely been incorporated into CA modeling to generate more realistic simulation patterns. We present a comparative study of four CA models incorporating logistic regression (LR) and the three metaheuristics respectively to simulate land use change in the Yangtze River Delta from 2005 to 2015. The metaheuristic processes are guided by an objective function that represents the root-mean-square error (RMSE) of the transition rules, which can then automatically search for suboptimal CA coefficients. The three metaheuristics are substantially different in terms of the algorithm mechanism, optimization iteration, and computational time. The land conversion potentials from the metaheuristics are similar in global patterns but marginally different in local regions, which substantially differ from that calculated using LR. All three metaheuristic CA models simulated slightly less than the reference change while the LA-CA model simulated substantially more than the reference change, however all models allocated the change to similar places. Our study shows that the three metaheuristics can achieve similar outcomes in the optimization of CA transition rules and land use simulation, albeit with different sensitivities to their intrinsic control parameters. We suggest that any of the three metaheuristics could be used to construct land use CA models, if the algorithm complexity and computational time are not highly concerned.
机译：元胞自动机（CA）是一种自下而上的建模框架，该框架已越来越多地用于通过捕获其动态来模拟土地使用变化。元启发法（例如粒子群优化（PSO），广义模拟退火（GSA）和遗传算法（GA））已被广泛地集成到CA建模中，以生成更现实的仿真模式。我们目前对四种分别采用logistic回归（LR）和三种元启发法的CA模型进行比较研究，以模拟长江三角洲2005年至2015年的土地利用变化。元启发法过程由代表均方根的目标函数指导转换规则的均方误差（RMSE），然后可以自动搜索次优的CA系数。这三种元启发式算法在算法机制，优化迭代和计算时间方面有很大不同。元启发法的土地转化潜力在全球格局上相似，但在局部地区则略有不同，这与使用LR计算得出的差异很大。所有三个元启发式CA模型的模拟量均比参考更改略少，而LA-CA模型的模拟量明显大于参考量更改，但是所有模型均将变化分配给相似的位置。我们的研究表明，这三种元启发法在优化CA过渡规则和土地利用模拟方面可以取得相似的结果，尽管对其内在控制参数的敏感性不同。我们建议，如果算法复杂度和计算时间不是很重要，则可以使用这三种元启发法中的任何一种来构建土地利用CA模型。

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