File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Spatial multi-objective land use optimization: Extensions to the non-dominated sorting genetic algorithm-II

TitleSpatial multi-objective land use optimization: Extensions to the non-dominated sorting genetic algorithm-II
Authors
KeywordsChina
land use planning
multi-objective optimization
NSGA-II-MOLU
planning support systems
spatial land use optimization
Tongzhou New Town
Issue Date2011
Citation
International Journal of Geographical Information Science, 2011, v. 25, n. 12, p. 1949-1969 How to Cite?
AbstractA spatial multi-objective land use optimization model defined by the acronym 'NSGA-II-MOLU' or the 'non-dominated sorting genetic algorithm-II for multi-objective optimization of land use' is proposed for searching for optimal land use scenarios which embrace multiple objectives and constraints extracted from the requirements of users, as well as providing support to the land use planning process. In this application, we took the MOLU model which was initially developed to integrate multiple objectives and coupled this with a revised version of the genetic algorithm NSGA-II which is based on specific crossover and mutation operators. The resulting NSGA-II-MOLU model is able to offer the possibility of efficiently searching over tens of thousands of solutions for trade-off sets which define non-dominated plans on the classical Pareto frontier. In this application, we chose the example of Tongzhou New Town, China, to demonstrate how the model could be employed to meet three conflicting objectives based on minimizing conversion costs, maximizing accessibility, and maximizing compatibilities between land uses. Our case study clearly shows the ability of the model to generate diversified land use planning scenarios which form the core of a land use planning support system. It also demonstrates the potential of the model to consider more complicated spatial objectives and variables with open-ended characteristics. The breakthroughs in spatial optimization that this model provides lead directly to other properties of the process in which further efficiencies in the process of optimization, more vivid visualizations, and more interactive planning support are possible. These form directions for future research. © 2011 Copyright Taylor and Francis Group, LLC.
Persistent Identifierhttp://hdl.handle.net/10722/329240
ISSN
2023 Impact Factor: 4.3
2023 SCImago Journal Rankings: 1.436
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorCao, Kai-
dc.contributor.authorBatty, Michael-
dc.contributor.authorHuang, Bo-
dc.contributor.authorLiu, Yan-
dc.contributor.authorYu, Le-
dc.contributor.authorChen, Jiongfeng-
dc.date.accessioned2023-08-09T03:31:23Z-
dc.date.available2023-08-09T03:31:23Z-
dc.date.issued2011-
dc.identifier.citationInternational Journal of Geographical Information Science, 2011, v. 25, n. 12, p. 1949-1969-
dc.identifier.issn1365-8816-
dc.identifier.urihttp://hdl.handle.net/10722/329240-
dc.description.abstractA spatial multi-objective land use optimization model defined by the acronym 'NSGA-II-MOLU' or the 'non-dominated sorting genetic algorithm-II for multi-objective optimization of land use' is proposed for searching for optimal land use scenarios which embrace multiple objectives and constraints extracted from the requirements of users, as well as providing support to the land use planning process. In this application, we took the MOLU model which was initially developed to integrate multiple objectives and coupled this with a revised version of the genetic algorithm NSGA-II which is based on specific crossover and mutation operators. The resulting NSGA-II-MOLU model is able to offer the possibility of efficiently searching over tens of thousands of solutions for trade-off sets which define non-dominated plans on the classical Pareto frontier. In this application, we chose the example of Tongzhou New Town, China, to demonstrate how the model could be employed to meet three conflicting objectives based on minimizing conversion costs, maximizing accessibility, and maximizing compatibilities between land uses. Our case study clearly shows the ability of the model to generate diversified land use planning scenarios which form the core of a land use planning support system. It also demonstrates the potential of the model to consider more complicated spatial objectives and variables with open-ended characteristics. The breakthroughs in spatial optimization that this model provides lead directly to other properties of the process in which further efficiencies in the process of optimization, more vivid visualizations, and more interactive planning support are possible. These form directions for future research. © 2011 Copyright Taylor and Francis Group, LLC.-
dc.languageeng-
dc.relation.ispartofInternational Journal of Geographical Information Science-
dc.subjectChina-
dc.subjectland use planning-
dc.subjectmulti-objective optimization-
dc.subjectNSGA-II-MOLU-
dc.subjectplanning support systems-
dc.subjectspatial land use optimization-
dc.subjectTongzhou New Town-
dc.titleSpatial multi-objective land use optimization: Extensions to the non-dominated sorting genetic algorithm-II-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/13658816.2011.570269-
dc.identifier.scopuseid_2-s2.0-84858795122-
dc.identifier.volume25-
dc.identifier.issue12-
dc.identifier.spage1949-
dc.identifier.epage1969-
dc.identifier.eissn1362-3087-
dc.identifier.isiWOS:000298360200003-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats