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Article: Integration of genetic algorithms and GIS for optimal location search
Title | Integration of genetic algorithms and GIS for optimal location search |
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Authors | |
Keywords | Genetic algorithms GIS Multiple objectives Optimal location Simulated annealing |
Issue Date | 2005 |
Publisher | Taylor & 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? |
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. © 2005 Taylor & Francis Group Ltd. |
Persistent Identifier | http://hdl.handle.net/10722/89873 |
ISSN | 2023 Impact Factor: 4.3 2023 SCImago Journal Rankings: 1.436 |
ISI Accession Number ID | |
References |
DC Field | Value | Language |
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dc.contributor.author | Li, X | en_HK |
dc.contributor.author | Yeh, AGO | en_HK |
dc.date.accessioned | 2010-09-06T10:02:51Z | - |
dc.date.available | 2010-09-06T10:02:51Z | - |
dc.date.issued | 2005 | en_HK |
dc.identifier.citation | International Journal Of Geographical Information Science, 2005, v. 19 n. 5, p. 581-601 | en_HK |
dc.identifier.issn | 1365-8816 | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/89873 | - |
dc.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. © 2005 Taylor & Francis Group Ltd. | en_HK |
dc.language | eng | en_HK |
dc.publisher | Taylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/13658816.asp | en_HK |
dc.relation.ispartof | International Journal of Geographical Information Science | en_HK |
dc.subject | Genetic algorithms | en_HK |
dc.subject | GIS | en_HK |
dc.subject | Multiple objectives | en_HK |
dc.subject | Optimal location | en_HK |
dc.subject | Simulated annealing | en_HK |
dc.title | Integration of genetic algorithms and GIS for optimal location search | en_HK |
dc.type | Article | en_HK |
dc.identifier.openurl | http://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+Search | en_HK |
dc.identifier.email | Yeh, AGO: hdxugoy@hkucc.hku.hk | en_HK |
dc.identifier.authority | Yeh, AGO=rp01033 | en_HK |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1080/13658810500032388 | en_HK |
dc.identifier.scopus | eid_2-s2.0-20444459508 | en_HK |
dc.identifier.hkuros | 107742 | en_HK |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-20444459508&selection=ref&src=s&origin=recordpage | en_HK |
dc.identifier.volume | 19 | en_HK |
dc.identifier.issue | 5 | en_HK |
dc.identifier.spage | 581 | en_HK |
dc.identifier.epage | 601 | en_HK |
dc.identifier.isi | WOS:000229793600004 | - |
dc.publisher.place | United Kingdom | en_HK |
dc.identifier.scopusauthorid | Li, X=34872691500 | en_HK |
dc.identifier.scopusauthorid | Yeh, AGO=7103069369 | en_HK |
dc.identifier.citeulike | 230263 | - |
dc.identifier.issnl | 1365-8816 | - |