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Article: Optimal spatial search using genetic algorithms and GIS

TitleOptimal spatial search using genetic algorithms and GIS
遺傳算法和GIS結合進行空間優化決策
Authors
KeywordsGenetic Algorithms (遺傳算法)
GIS (GIS)
Simulated Annealing (退火算法)
Spatial Optimization (空間優化)
Issue Date2004
PublisherScience Press (科學出版社). The Journal's web site is located at http://www.geog.com.cn/
Citation
Acta Geographica Sinica, 2004, v. 59 n. 5, p. 745-753 How to Cite?
地理學報, 2004, v. 59 n. 5, p. 745-753 How to Cite?
AbstractThis study demonstrates that genetic algorithms are capable of producing satisfying results for optimal spatial search under complex situations. We successfully solve a spatial search problem using the proposed method to allocate the facility according to the population constraint from GIS. The search algorithm is very simple using the mechanics of natural selection in biology. The proposed method can be used as a planning tool that can help urban planners to improve development efficiency in site selection. The method is developed by a common computer language which can directly use the full functions of a commercial GA package through the DLL and can import the spatial data from GIS. This integration is useful for solving realistic problems by using large spatial data sets. The programming can be easily adapted to other applications by just modifying the fitness functions instead of changing the model itself. The proposed method has been tested in the city of Hong Kong, a densely populated region. The population data are obtained from the census department and the population density is prepared in GIS as the main inputs to the GA programming.
資源的有效利用和管理往往涉及到空間的優化配置問題。例如需要在空間上確定n個設施的最佳位置。當選址問題涉及多個目標和不同的約束性條件時,就會變得十分復雜。利用一般的brute-force搜索方法無法對涉及高維數據的問題進行求解。利用遺傳算法和GIS結合來解決復雜的空間優化配置問題,具有智能的搜索方法可以大大提高空間的搜索能力。在基于進化的優化過程中,根據GIS的空間數據來計算不同解決方案(染色體) 的適應度。針對不同的應用目的,GIS可以給出不同的適應度函數。實驗表明,所提出的方法比簡單的搜索方法和退火算法有更大的優越性。該方法在處理復雜的空間優化問題有更好的表現。
Persistent Identifierhttp://hdl.handle.net/10722/176285
ISSN
2015 SCImago Journal Rankings: 0.447
References

 

DC FieldValueLanguage
dc.contributor.authorLi, Xen_US
dc.contributor.authorYeh, AGOen_US
dc.date.accessioned2012-11-26T09:08:13Z-
dc.date.available2012-11-26T09:08:13Z-
dc.date.issued2004en_US
dc.identifier.citationActa Geographica Sinica, 2004, v. 59 n. 5, p. 745-753en_US
dc.identifier.citation地理學報, 2004, v. 59 n. 5, p. 745-753-
dc.identifier.issn0375-5444en_US
dc.identifier.urihttp://hdl.handle.net/10722/176285-
dc.description.abstractThis study demonstrates that genetic algorithms are capable of producing satisfying results for optimal spatial search under complex situations. We successfully solve a spatial search problem using the proposed method to allocate the facility according to the population constraint from GIS. The search algorithm is very simple using the mechanics of natural selection in biology. The proposed method can be used as a planning tool that can help urban planners to improve development efficiency in site selection. The method is developed by a common computer language which can directly use the full functions of a commercial GA package through the DLL and can import the spatial data from GIS. This integration is useful for solving realistic problems by using large spatial data sets. The programming can be easily adapted to other applications by just modifying the fitness functions instead of changing the model itself. The proposed method has been tested in the city of Hong Kong, a densely populated region. The population data are obtained from the census department and the population density is prepared in GIS as the main inputs to the GA programming.en_US
dc.description.abstract資源的有效利用和管理往往涉及到空間的優化配置問題。例如需要在空間上確定n個設施的最佳位置。當選址問題涉及多個目標和不同的約束性條件時,就會變得十分復雜。利用一般的brute-force搜索方法無法對涉及高維數據的問題進行求解。利用遺傳算法和GIS結合來解決復雜的空間優化配置問題,具有智能的搜索方法可以大大提高空間的搜索能力。在基于進化的優化過程中,根據GIS的空間數據來計算不同解決方案(染色體) 的適應度。針對不同的應用目的,GIS可以給出不同的適應度函數。實驗表明,所提出的方法比簡單的搜索方法和退火算法有更大的優越性。該方法在處理復雜的空間優化問題有更好的表現。-
dc.languagechien_US
dc.publisherScience Press (科學出版社). The Journal's web site is located at http://www.geog.com.cn/-
dc.relation.ispartofActa Geographica Sinicaen_US
dc.relation.ispartof地理學報-
dc.subjectGenetic Algorithms (遺傳算法)en_US
dc.subjectGIS (GIS)en_US
dc.subjectSimulated Annealing (退火算法)en_US
dc.subjectSpatial Optimization (空間優化)en_US
dc.titleOptimal spatial search using genetic algorithms and GISen_US
dc.title遺傳算法和GIS結合進行空間優化決策-
dc.typeArticleen_US
dc.identifier.emailYeh, AGO: hdxugoy@hkucc.hku.hken_US
dc.identifier.authorityYeh, AGO=rp01033en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-12844281860en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-12844281860&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume59en_US
dc.identifier.issue5en_US
dc.identifier.spage745en_US
dc.identifier.epage753en_US
dc.publisher.placeBeijing (北京)en_US
dc.identifier.scopusauthoridLi, X=34872691500en_US
dc.identifier.scopusauthoridYeh, AGO=7103069369en_US

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