Journal
COMPUTERS ENVIRONMENT AND URBAN SYSTEMS
Volume 36, Issue 3, Pages 257-269Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.compenvurbsys.2011.08.001
Keywords
Land use optimization; Genetic algorithm; Sustainability; Spatial compactness; Reference point; Tongzhou Newtown
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Funding
- Office of Advanced Cyberinfrastructure (OAC)
- Direct For Computer & Info Scie & Enginr [1047916] Funding Source: National Science Foundation
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Under the notion of sustainable development, a heuristic method named as the Boundary-based Fast Genetic Algorithm (BFGA) is developed to search for optimal solutions to a land use allocation problem with multiple objectives and constraints. Plans are obtained based on the trade-off among economic benefit, environmental and ecological benefit, social equity including Gross Domestic Product (GDP), conversion cost, geological suitability, ecological suitability, accessibility, Not In My Back Yard (NIMBY) influence, compactness, and compatibility. These objectives and constraints are formulated into a Multi-objective Optimization of Land Use (MOLU) model based on a reference point method (i.e. goal programming). This paper demonstrates that the BFGA is effective by offering the possibility of searching over tens of thousands of plans for trade-off sets of non-dominated plans. This paper presents an application of the model to the Tongzhou Newtown in Beijing, China. The results clearly evince the potential of the model in a planning support process by generating suggested near-optimal planning scenarios considering multi-objectives with different preferences. Published by Elsevier Ltd.
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