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

TitleIntegration of genetic algorithms and GIS for optimal location search
Authors
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
Citation
International Journal Of Geographical Information Science, 2005, v. 19 n. 5, p. 581-601 How to Cite?
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.
Persistent Identifierhttp://hdl.handle.net/10722/89873
ISSN
2015 Impact Factor: 2.065
2015 SCImago Journal Rankings: 1.156
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLi, Xen_HK
dc.contributor.authorYeh, AGOen_HK
dc.date.accessioned2010-09-06T10:02:51Z-
dc.date.available2010-09-06T10:02:51Z-
dc.date.issued2005en_HK
dc.identifier.citationInternational Journal Of Geographical Information Science, 2005, v. 19 n. 5, p. 581-601en_HK
dc.identifier.issn1365-8816en_HK
dc.identifier.urihttp://hdl.handle.net/10722/89873-
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.en_HK
dc.languageengen_HK
dc.publisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/13658816.aspen_HK
dc.relation.ispartofInternational Journal of Geographical Information Scienceen_HK
dc.subjectGenetic algorithmsen_HK
dc.subjectGISen_HK
dc.subjectMultiple objectivesen_HK
dc.subjectOptimal locationen_HK
dc.subjectSimulated annealingen_HK
dc.titleIntegration of genetic algorithms and GIS for optimal location searchen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1365-8816&volume=19&issue=5&spage=581&epage=601&date=2005&atitle=Integration+of+Genetic+Algorithms+and+GIS+for+Optimal+Location+Searchen_HK
dc.identifier.emailYeh, AGO: hdxugoy@hkucc.hku.hken_HK
dc.identifier.authorityYeh, AGO=rp01033en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/13658810500032388en_HK
dc.identifier.scopuseid_2-s2.0-20444459508en_HK
dc.identifier.hkuros107742en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-20444459508&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume19en_HK
dc.identifier.issue5en_HK
dc.identifier.spage581en_HK
dc.identifier.epage601en_HK
dc.identifier.isiWOS:000229793600004-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridLi, X=34872691500en_HK
dc.identifier.scopusauthoridYeh, AGO=7103069369en_HK
dc.identifier.citeulike230263-

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