Article: Integration of genetic algorithms and GIS for optimal location search

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TitleIntegration of genetic algorithms and GIS for optimal location search
AuthorsLi, X2
Yeh, AGO1
KeywordsGenetic algorithms
GIS
Multiple objectives
Optimal location
Simulated annealing
Issue Date2005
PublisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/13658816.asp
CitationInternational Journal Of Geographical Information Science, 2005, v. 19 n. 5, p. 581-601 [How to Cite?]
DOI: http://dx.doi.org/10.1080/13658810500032388
AbstractOptimal location search is frequently required in many urban applications for siting one or more facilities. However, the search may become very complex when it involves multiple sites, various constraints and multiple-objectives. The exhaustive blind (brute-force) search with high-dimensional spatial data is infeasible in solving optimization problems because of a huge combinatorial solution space. Inteligent search algorithms can help to improve the performance of spatial search. This study will demonstrate that genetic algorithms can be used with Geographical Information systems (GIS) to effectively solve the spatial decision problems for optimally sitting n sites of a facility. Detailed population and transportation data from GIS are used to facilitate the calculation of fitness functions. Multiple planning objectives are also incorporated in the GA program. Experiments indicate that the proposed method has much better performance than simulated annealing and GIS neighborhood search methods. The GA method is very convenient in finding the solution with the highest utility value. © 2005 Taylor & Francis Group Ltd.
ISSN1365-8816
2011 Impact Factor: 1.472
2011 SCImago Journal Rankings: 0.077
DOIhttp://dx.doi.org/10.1080/13658810500032388
ISI Accession Number IDWOS:000229793600004
ReferencesReferences in Scopus
DC Field
Value
dc.contributor.authorLi, X
dc.contributor.authorYeh, AGO
dc.date.accessioned2010-09-06T10:02:51Z
dc.date.available2010-09-06T10:02:51Z
dc.date.issued2005
dc.description.abstractOptimal location search is frequently required in many urban applications for siting one or more facilities. However, the search may become very complex when it involves multiple sites, various constraints and multiple-objectives. The exhaustive blind (brute-force) search with high-dimensional spatial data is infeasible in solving optimization problems because of a huge combinatorial solution space. Inteligent search algorithms can help to improve the performance of spatial search. This study will demonstrate that genetic algorithms can be used with Geographical Information systems (GIS) to effectively solve the spatial decision problems for optimally sitting n sites of a facility. Detailed population and transportation data from GIS are used to facilitate the calculation of fitness functions. Multiple planning objectives are also incorporated in the GA program. Experiments indicate that the proposed method has much better performance than simulated annealing and GIS neighborhood search methods. The GA method is very convenient in finding the solution with the highest utility value. © 2005 Taylor & Francis Group Ltd.
dc.description.natureLink_to_subscribed_fulltext
dc.identifier.citationInternational Journal Of Geographical Information Science, 2005, v. 19 n. 5, p. 581-601 [How to Cite?]
DOI: http://dx.doi.org/10.1080/13658810500032388
dc.identifier.citeulike230263
dc.identifier.doihttp://dx.doi.org/10.1080/13658810500032388
dc.identifier.epage601
dc.identifier.hkuros107742
dc.identifier.isiWOS:000229793600004
dc.identifier.issn1365-8816
2011 Impact Factor: 1.472
2011 SCImago Journal Rankings: 0.077
dc.identifier.issue5
dc.identifier.openurl
dc.identifier.scopuseid_2-s2.0-20444459508
dc.identifier.spage581
dc.identifier.urihttp://hdl.handle.net/10722/89873
dc.identifier.volume19
dc.languageeng
dc.publisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/13658816.asp
dc.publisher.placeUnited Kingdom
dc.relation.ispartofInternational Journal of Geographical Information Science
dc.relation.referencesReferences in Scopus
dc.subjectGenetic algorithms
dc.subjectGIS
dc.subjectMultiple objectives
dc.subjectOptimal location
dc.subjectSimulated annealing
dc.titleIntegration of genetic algorithms and GIS for optimal location search
dc.typeArticle
Author Affiliations
  1. The University of Hong Kong
  2. Sun Yat-Sen University