基于GA-PSO融合算法的舰船轮廓CAD模型参数优化-豆柴文库

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基于GA-PSO融合算法的舰船轮廓CAD模型参数优化
Title:ParameterOptimizationofShipProfileCADModelsBasedonGA-PSOFusionAlgorithm
Abstract:
Withthecontinuousadvancementofshipbuildingtechnology,thedesignofshipprofileCADmodelsplaysacrucialroleinshipmanufacturing.However,findingtheoptimalparametersforshipprofileCADmodelsisacomplextaskduetotheinterdependenceofmultipledesignvariablesandconstraints.
ThispaperproposesanovelapproachtooptimizeshipprofileCADmodelparametersusingafusionofGeneticAlgorithm(GA)andParticleSwarmOptimization(PSO),calledGA-PSOfusionalgorithm.TheGA-PSOfusionalgorithmcombinestheglobalsearchcapabilityofGAwiththerapidconvergenceofPSO,providinganeffectivesolutionforparameteroptimizationproblems.
Inthisstudy,theshipprofileCADmodelsarerepresentedbyasetofparameters,includingbowshape,bulgeplacement,keelcurve,andsternshape.Theobjectivefunctionconsideredistheminimizationofhydrodynamicresistance,whichisacriticalfactorforshipperformance.Thedesignvariablesandconstraintsaredefinedbasedonengineeringspecificationsandrequirements.
IntheGA-PSOfusionalgorithm,thepopulationinitializationiscarriedoutusingGA,creatinganinitialpopulationthatrepresentsthediversesolutionsinthesearchspace.Thefitnessvaluesoftheindividualsintheinitialpopulationareevaluatedbasedontheobjectivefunction.ThePSOalgorithmisthenappliedtoupdatetheindividuals'velocitiesandpositionsbasedontheirpersonalbestandglobalbestinformation,promotingtheexplorationandexploitationofthesearchspace.TheGAandPSOcomponentsiterativelyinteracttorefineandoptimizetheparametervaluesoftheshipprofileCADmodels.
TovalidatetheeffectivenessoftheproposedGA-PSOfusionalgorithm,itisappliedtoacasestudyinvolvingtheoptimizationofashipprofileCADmodelforacontainership.TheresultsdemonstratethattheGA-PSOfusionalgorithmcansuccessfullyconvergetotheoptimalparametervaluesthatminimizehydrodynamicresistancewhilesatisfyingthedesignconstraints.TheoptimizedshipprofileCADmodelexhibitsimprovedperformancecomparedtotheinitialdesign.
Furthermore,asensitivityanalysisisconductedtoevaluat