Sustainable land use optimization using Boundary-based Fast Genetic Algorithm

• Main Authors: CAO, Kai, HUANG, Bo, WANG, Shaowen, LIN, Hui
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2012
• Subjects: Land use optimization; Genetic algorithm; Sustainability; Spatial compactness; Reference point; Tongzhou Newtown; Databases and Information Systems; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/5407; https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=6410&context=sis_research
• Institution: Singapore Management University

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.