Sustainable land use optimization using Boundary-based Fast Genetic Algorithm

Kai Cao, Bo Huang, Shaowen Wang, Hui Lin

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)257-269
Number of pages13
JournalComputers, Environment and Urban Systems
Issue number3
StatePublished - May 2012


  • Genetic algorithm
  • Land use optimization
  • Reference point
  • Spatial compactness
  • Sustainability
  • Tongzhou Newtown

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Ecological Modeling
  • Environmental Science(all)
  • Urban Studies


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