File Download
 
Links for fulltext
(May Require Subscription)
 
Supplementary

Article: Integration of genetic algorithms and GIS for optimal location search
  • Basic View
  • Metadata View
  • XML View
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
2013 Impact Factor: 1.479
 
DOIhttp://dx.doi.org/10.1080/13658810500032388
 
ISI Accession Number IDWOS:000229793600004
 
ReferencesReferences in Scopus
 
DC FieldValue
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
2013 Impact Factor: 1.479
 
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
 
<?xml encoding="utf-8" version="1.0"?>
<item><contributor.author>Li, X</contributor.author>
<contributor.author>Yeh, AGO</contributor.author>
<date.accessioned>2010-09-06T10:02:51Z</date.accessioned>
<date.available>2010-09-06T10:02:51Z</date.available>
<date.issued>2005</date.issued>
<identifier.citation>International Journal Of Geographical Information Science, 2005, v. 19 n. 5, p. 581-601</identifier.citation>
<identifier.issn>1365-8816</identifier.issn>
<identifier.uri>http://hdl.handle.net/10722/89873</identifier.uri>
<description.abstract>Optimal 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. &#169; 2005 Taylor &amp; Francis Group Ltd.</description.abstract>
<language>eng</language>
<publisher>Taylor &amp; Francis Ltd. The Journal&apos;s web site is located at http://www.tandf.co.uk/journals/titles/13658816.asp</publisher>
<relation.ispartof>International Journal of Geographical Information Science</relation.ispartof>
<subject>Genetic algorithms</subject>
<subject>GIS</subject>
<subject>Multiple objectives</subject>
<subject>Optimal location</subject>
<subject>Simulated annealing</subject>
<title>Integration of genetic algorithms and GIS for optimal location search</title>
<type>Article</type>
<identifier.openurl>http://library.hku.hk:4550/resserv?sid=HKU:IR&amp;issn=1365-8816&amp;volume=19&amp;issue=5&amp;spage=581&amp;epage=601&amp;date=2005&amp;atitle=Integration+of+Genetic+Algorithms+and+GIS+for+Optimal+Location+Search</identifier.openurl>
<description.nature>Link_to_subscribed_fulltext</description.nature>
<identifier.doi>10.1080/13658810500032388</identifier.doi>
<identifier.scopus>eid_2-s2.0-20444459508</identifier.scopus>
<identifier.hkuros>107742</identifier.hkuros>
<relation.references>http://www.scopus.com/mlt/select.url?eid=2-s2.0-20444459508&amp;selection=ref&amp;src=s&amp;origin=recordpage</relation.references>
<identifier.volume>19</identifier.volume>
<identifier.issue>5</identifier.issue>
<identifier.spage>581</identifier.spage>
<identifier.epage>601</identifier.epage>
<identifier.isi>WOS:000229793600004</identifier.isi>
<publisher.place>United Kingdom</publisher.place>
<identifier.citeulike>230263</identifier.citeulike>
</item>
Author Affiliations
  1. The University of Hong Kong
  2. Sun Yat-Sen University