Half a Billion Simulations: Evolutionary Algorithms and Distributed Computing for Calibrating the Simpoplocal Geographical Model
Half a Billion Simulations: Evolutionary Algorithms and Distributed Computing for Calibrating the Simpoplocal Geographical Model
复制标题
5 亿次模拟：用于校准 Simpoplocal 地理模型的进化算法和分布式计算
DOI：
10.1068/b130064p
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
D. Pumain
中科院分区：
文献类型：
--
作者：
Clara Schmitt;Sebastien Rey;Romain Reuillon;D. Pumain
多智能体地理模型集成了大量的空间相互作用。为了验证这些模型，需要进行大量的模拟和标定计算。在这里，一个新的数据处理链，包括一个自动校准程序，使用进化算法在计算网格上进行测试。这是第一次应用于一个旨在模拟早期城市住区系统演变的地理模型。该方法使我们能够减少计算时间，并提供鲁棒性的结果。使用这种方法，我们确定了几个参数设置，这些设置最小化了三个目标函数，这些目标函数量化了模型结果与参考模式的匹配程度。由于每个参数在不同设置下的值非常接近，这种估计大大减少了参数的初始可能变化域。因此，该模型是一个有用的工具，可以进一步在经验历史情况下进行多种应用。
Multiagent geographical models integrate very large numbers of spatial interactions. In order to validate these models a large amount of computing is necessary for their simulation and calibration. Here a new data-processing chain, including an automated calibration procedure, is tested on a computational grid using evolutionary algorithms. This is applied for the first time to a geographical model designed to simulate the evolution of an early urban settlement system. The method enables us to reduce the computing time and provides robust results. Using this method, we identify several parameter settings that minimize three objective functions that quantify how closely the model results match a reference pattern. As the values of each parameter in different settings are very close, this estimation considerably reduces the initial possible domain of variation of the parameters. Thus the model is a useful tool for further multiple applications in empirical historical situations.