Using simulated annealing and genetic algorithms for solving a multi site land use allocation problem

J.C.J.H. Aerts, M. van Herwijnen, R. Janssen, T.J. Stewart

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

This study examines the use of spatial optimization techniques for multi-site land-use allocation problems (MLUA). 'Multi-site' refers to the problem of allocating more than one land-use type in an area, which are difficult problems as they involve multiple stakeholders with conflicting goals and objectives. Spatial optimization methods consist of (1) an optimization model and (2) an algorithm to solve the model. This study demonstrates a goal-programming model to solve the MLUA problem. The model is solved using both simulated annealing and genetic algorithms. Special attention has been given to introduce a spatial compactness objective in the model. It is shown that the compactness objectives in the optimization model generate compact patches of the same land use for using both the simulated annealing procedure and the genetic algorithm. In addition, it appears that using the proper settings of the compactness objectives, connectivity between patches of land use is promoted. The method is tested for a fictive study and then demonstrated for a real case study, both measuring 20 × 20 cells. The genetic algorithm generally performs better than simulated annealing in terms of solution time and achieving compactness. © 2005 University of Newcastle upon Tyne.
Original languageEnglish
Pages (from-to)121-142
Number of pages22
JournalJournal of Environmental Planning and Management
Volume48
Issue number1
DOIs
Publication statusPublished - 2005

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simulated annealing
Simulated annealing
Land use
genetic algorithm
land use
Genetic algorithms
optimization model
programming
stakeholder
allocation
connectivity

Cite this

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title = "Using simulated annealing and genetic algorithms for solving a multi site land use allocation problem",
abstract = "This study examines the use of spatial optimization techniques for multi-site land-use allocation problems (MLUA). 'Multi-site' refers to the problem of allocating more than one land-use type in an area, which are difficult problems as they involve multiple stakeholders with conflicting goals and objectives. Spatial optimization methods consist of (1) an optimization model and (2) an algorithm to solve the model. This study demonstrates a goal-programming model to solve the MLUA problem. The model is solved using both simulated annealing and genetic algorithms. Special attention has been given to introduce a spatial compactness objective in the model. It is shown that the compactness objectives in the optimization model generate compact patches of the same land use for using both the simulated annealing procedure and the genetic algorithm. In addition, it appears that using the proper settings of the compactness objectives, connectivity between patches of land use is promoted. The method is tested for a fictive study and then demonstrated for a real case study, both measuring 20 × 20 cells. The genetic algorithm generally performs better than simulated annealing in terms of solution time and achieving compactness. {\circledC} 2005 University of Newcastle upon Tyne.",
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Using simulated annealing and genetic algorithms for solving a multi site land use allocation problem. / Aerts, J.C.J.H.; van Herwijnen, M.; Janssen, R.; Stewart, T.J.

In: Journal of Environmental Planning and Management, Vol. 48, No. 1, 2005, p. 121-142.

Research output: Contribution to JournalArticleAcademicpeer-review

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