Coupling Simulation and Optimization to Solve Planning Problems in a Fast-Developing Area

In geographical analysis, spatial simulation and optimization are usually separate processes tackling different problems. It is, however, increasingly necessary to integrate them. Particularly in a fast developing area, the development to be simulated is seldom inertial (i.e., strictly following the...

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Bibliographic Details
Published in:	Annals of the Association of American Geographers Vol. 101; no. 5; pp. 1032 - 1048
Main Authors:	Li, Xia, Shi, Xun, He, Jinqiang, Liu, Xaioping
Format:	Journal Article
Language:	English
Published:	Washington, DC Taylor & Francis Group 01.09.2011 Association of American Geographers Taylor & Francis Ltd
Subjects:	ant colony optimization Ants Automatic cartography autómata celular Bgi / Prodig Cartography, space figuration Case studies Cellular automata China Economic geography Expressways Geographic information systems Geography Industrial output Insect colonies Land use land use simulation Measurement Methodology Methods, Models, and GIS model coupling Modeling modelo de acoplamiento optimización de hormiguero optimización de ruta Optimization path optimization Pheromones Planning methods simulación del uso del suelo Simulation Simulations Spatial analysis Urban development 元胞自动机土地使用模拟模型耦合蚁群优化算法路径优化 Guangdong China Industrialization Simulation Urban planning Spatial analysis Motorway Urban system Urban area Automated mapping Freight Optimization Land use
ISSN:	0004-5608, 2469-4452, 1467-8306, 2469-4460
Online Access:	Get full text
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Summary:	In geographical analysis, spatial simulation and optimization are usually separate processes tackling different problems. It is, however, increasingly necessary to integrate them. Particularly in a fast developing area, the development to be simulated is seldom inertial (i.e., strictly following the historical trend); instead, it is likely to be interfered by new planning measures. Meanwhile, in such an area an optimization plan might not be even meaningful if it only addresses a snapshot of a highly dynamic landscape. In this study, we explored the possibility of integrating cellular automata (CA), a widely used method for simulating urban development and land use changes, and ant colony optimization (ACO), an advanced technique for solving complex path optimization problems. We named the resulting integrated system the geographical simulation and optimization system (GeoSOS) and applied it to a case study concerning finding the optimal path for a planned expressway in Dongguan, a fast-growing city in one of the most economically active regions of China. In the case study, the CA component of the GeoSOS generated simulations of the industrial land use changes for some years in the next decade. The ACO component of the GeoSOS, which had been revised from the conventional ACO to work on raster surfaces, took the simulations as input and completed raster-based path optimizations. In terms of the cumulative utility, a measurement used to evaluate the performance of the optimization, the coupling method surpasses the noncoupling method by 10.3 percent.
Bibliography:	SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-2 content type line 23
ISSN:	0004-5608 2469-4452 1467-8306 2469-4460
DOI:	10.1080/00045608.2011.577366