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Article: Discovery of transition rules for geographical cellular automata by using ant colony optimization

TitleDiscovery of transition rules for geographical cellular automata by using ant colony optimization
Authors
KeywordsAnt Colony Optimization
Artificial Intelligence
Ca
Geographical Simulation
Issue Date2007
PublisherScience China Press. The Journal's web site is located at http://link.springer.com/journal/11430
Citation
Science In China, Series D: Earth Sciences, 2007, v. 50 n. 10, p. 1578-1588 How to Cite?
AbstractA new intelligent algorithm of geographical cellular automata (CA) based on ant colony optimization (ACO) is proposed in this paper. CA is capable of simulating the evolution of complex geographical phenomena, and the core of CA models is how to define transition rules. However, most of the transition rules are defined by mathematical equations, and are hence not explicit. When the study area is complicated, it is much more difficult to extract parameters for geographical CA. As a result, ACO is applied to geographical CA to automatically and intelligently obtain transition rules in this paper. The transition rules extracted by ACO are defined as logical expressions rather than implicit mathematical equations to describe the complex relationships of the nature, and easy for people to understand. The ACO-CA model was applied to simulating rural-urban land conversions in Guangzhou City, China, and appropriate simulation results were generated. Compared with See5.0 decision tree model, ACO-CA is more suitable to discovering transition rules for geographical CA. © Science in China Press 2007.
Persistent Identifierhttp://hdl.handle.net/10722/176289
ISSN
2011 Impact Factor: 1.588
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLiu, XPen_US
dc.contributor.authorLi, Xen_US
dc.contributor.authorYeh, AGOen_US
dc.contributor.authorHe, JQen_US
dc.contributor.authorTao, Jen_US
dc.date.accessioned2012-11-26T09:08:14Z-
dc.date.available2012-11-26T09:08:14Z-
dc.date.issued2007en_US
dc.identifier.citationScience In China, Series D: Earth Sciences, 2007, v. 50 n. 10, p. 1578-1588en_US
dc.identifier.issn1006-9313en_US
dc.identifier.urihttp://hdl.handle.net/10722/176289-
dc.description.abstractA new intelligent algorithm of geographical cellular automata (CA) based on ant colony optimization (ACO) is proposed in this paper. CA is capable of simulating the evolution of complex geographical phenomena, and the core of CA models is how to define transition rules. However, most of the transition rules are defined by mathematical equations, and are hence not explicit. When the study area is complicated, it is much more difficult to extract parameters for geographical CA. As a result, ACO is applied to geographical CA to automatically and intelligently obtain transition rules in this paper. The transition rules extracted by ACO are defined as logical expressions rather than implicit mathematical equations to describe the complex relationships of the nature, and easy for people to understand. The ACO-CA model was applied to simulating rural-urban land conversions in Guangzhou City, China, and appropriate simulation results were generated. Compared with See5.0 decision tree model, ACO-CA is more suitable to discovering transition rules for geographical CA. © Science in China Press 2007.en_US
dc.languageengen_US
dc.publisherScience China Press. The Journal's web site is located at http://link.springer.com/journal/11430en_US
dc.relation.ispartofScience in China, Series D: Earth Sciencesen_US
dc.subjectAnt Colony Optimizationen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectCaen_US
dc.subjectGeographical Simulationen_US
dc.titleDiscovery of transition rules for geographical cellular automata by using ant colony optimizationen_US
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.doi10.1007/s11430-007-0083-zen_US
dc.identifier.scopuseid_2-s2.0-34548756106en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-34548756106&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume50en_US
dc.identifier.issue10en_US
dc.identifier.spage1578en_US
dc.identifier.epage1588en_US
dc.identifier.isiWOS:000250583000016-
dc.publisher.placeChinaen_US
dc.identifier.scopusauthoridLiu, XP=14521152600en_US
dc.identifier.scopusauthoridLi, X=34872584400en_US
dc.identifier.scopusauthoridYeh, AGO=7103069369en_US
dc.identifier.scopusauthoridHe, JQ=21742562100en_US
dc.identifier.scopusauthoridTao, J=55242635400en_US

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