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1、InternationalJournalofGeographicalInformationScienceVol.19,No.5,May2005,581–601ResearchArticleIntegrationofgeneticalgorithmsandGISforoptimallocationsearchXIALI{andANTHONYGAR-ONYEH{{SchoolofGeographyandPlanning,SunYat-senUniversity,135WestXingangRd.,Guangzhou510275,P.R.China
2、;e-mail:lixia@graduate.hku.hk;gplx@zsu.edu.cn{CentreofUrbanPlanningandEnvironmentalManagement,TheUniversityofHongKong,PokfulamRoad,HongKongSAR,P.R.China;e-mail:hdxugoy@hkucc.hku.hk(Received24February2004;accepted23September2004)Optimallocationsearchisfrequentlyrequiredinman
3、yurbanapplicationsforsitingoneormorefacilities.However,thesearchmaybecomeverycomplexwhenitinvolvesmultiplesites,variousconstraintsandmultiple-objectives.Theexhaustiveblind(brute-force)searchwithhigh-dimensionalspatialdataisinfeasibleinsolvingoptimizationproblemsbecauseofahu
4、gecombinatorialsolutionspace.Intelligentsearchalgorithmscanhelptoimprovetheperformanceofspatialsearch.ThisstudywilldemonstratethatgeneticalgorithmscanbeusedwithGeographicalInformationsystems(GIS)toeffectivelysolvethespatialdecisionproblemsforoptimallysittingnsitesofafacilit
5、y.DetailedpopulationandtransportationdatafromGISareusedtofacilitatethecalculationoffitnessfunctions.MultipleplanningobjectivesarealsoincorporatedintheGAprogram.ExperimentsindicatethattheproposedmethodhasmuchbetterperformancethansimulatedannealingandGISneighborhoodsearchmeth
6、ods.TheGAmethodisveryconvenientinfindingthesolutionwiththehighestutilityvalue.Keywords:Geneticalgorithms;GIS;Optimallocation;Multipleobjectives;Simulatedannealing1.IntroductionAnoftenencounteredspatialdecisionproblemistosearchforthebestsiteorsitestoaccommodateoneormorefacil
7、itiestogeneratethebestutilityvalues(e.g.themaximumpopulationcoverageandminimumtransportcost).Traditionallocation-allocationmethodsbeforeGISdatawereavailableonlyuserelativelysmalldatasets(Church1999).Thegeneralfacilitylocationproblemanditsvariants,includingmostlocation-alloc
8、ationandp-medianproblems,areknowntobeNP-hardcombinatorialoptimizationproblems.Most
