一種求解非線性約束優(yōu)化問題的無罰函數(shù)無濾子的方法

一種求解非線性約束優(yōu)化問題的無罰函數(shù)無濾子的方法Title:APenalty-freeandFilter-freeApproachforSolvingNonlinearConstrainedOptimizationProblemsAbstract:Nonlinearconstrainedoptimizationproblemsariseinvariousfields,suchasengineering,economics,andmachinelearning.Theseproblemsofteninvolvefindingtheoptimalvaluesofasetofdecisionvariableswhilesatisfyingasetofconstraints.Inthispaper,weproposeapenalty-freeandfilter-freeapproachforsolvingsuchproblems.Theproposedmethodavoidstheuseofpenaltyfunctionsandfilters,whichcanintroduceadditionalcomplexityandcomputationalcost.Instead,itleveragesthebenefitsofadirectsearchmethodcombinedwithanadaptivesamplingstrategytoefficientlyexplorethesolutionspace.Theeffectivenessoftheproposedapproachisdemonstratedthroughnumericalexperimentsonasetofbenchmarkproblems.1.Introduction:Nonlinearconstrainedoptimizationproblemscanbemathematicallyformulatedasminimizinganobjectivefunctionsubjecttoasetofconstraints.Traditionalmethodsforsolvingtheseproblemsofteninvolvetheuseofpenaltyfunctionsorfilterstotransformtheconstrainedproblemintoanunconstrainedone.However,theseapproachescanleadtodifficultiesinfindingtheglobalminimum,introduceadditionalcomplexity,andresultinahighercomputationalburden.Thispaperproposesapenalty-freeandfilter-freeapproachthatovercomestheselimitationsandprovidesanefficientsolutionstrategy.2.ProblemFormulation:Thenonlinearconstrainedoptimizationproblemisrepresentedasfollows:Minimize:f(x)Subjectto:g(x)≤0h(x)=0wheref(x)istheobjectivefunction,g(x)representsinequalityconstraints,h(x)depictsequalityconstraints,andxisthedecisionvariablevector.3.TheProposedMethod:Theproposedapproachisbasedonadirectsearchmethodthatiterativelyexploresthesolutionspacetofindtheoptimalvaluesofthedecisionvariables.Unliketraditionalmethods,nopenaltyfunctionsorfiltersareusedtohandletheconstraints.Instead,theapproachutilizesanadaptivesamplingstrategy,whichadaptivelyadjuststhesamplingpointsbasedontheevaluationresultstoguidethesearchtowardspromisingregionsofthesolutionspace.Thisadaptivesamplingstrategyallowsforamoreefficientexplorationofthefeasibleregionwhilesatisfyingtheconstraints.4.AdaptiveSamplingStrategy:Theadaptivesamplingstrategyconsistsoftwomaincomponents:explorationandexploitation.Intheexplorationphase,thealgorithminitiallysamplespointsrandomlyfromthefeasibleregionandevaluatestheobjectivefunctionandconstraintsatthesepoints.Theseevaluationsprovideinformationaboutthelandscapeandguidethesearchtowardspromisingregions.Intheexploitationphase,thealgorithmfocusesonrefiningthesolutionbysamplingnearthepromisingregionsbasedontheevaluationresults.Thisiterativeprocesscontinuesuntilaterminationcriterionismet.5.Algorithm:Thealgorithmforthepenalty-freeandfilter-freeapproachisoutlinedasfollows:1.Initializethedecisionvariablevectorx.2.Generateasetofinitialsamplingpointsrandomlywithinthefeasibleregion.3.Evaluatetheobjectivefunctionandconstraintsatthesepoints.4.Identifythebestpointbasedontheobjectivefunctionvalueandconstraintviolation.5.Adaptivelyadjustthesamplingpointsbasedontheevaluationresults.6.Repeatsteps3-5untilaterminationcriterionismet(e.g.,maximumnumberofiterations,convergencecheck).7.Outputthebestsolutionfound.6.ExperimentalResults:Toassesstheeffectivenessoftheproposedapproach,numericalexperimentsareconductedonasetofbenchmarkproblemsfromdifferentdomains.Theresultsarecomparedwithtraditionalpenalty-basedapproachesandfilter-basedapproaches.Theexperimentalresultsdemonstratethatthepenalty-freeandfilter-freeapproachoutperformsthetraditionalmethodsintermsofsolutionaccuracy,convergencespeed,andcomputationalefficiency.Italsoshowsrobustnesstodifferentproblemtypesanddimensions.7.Conclusion:Inthispaper,apenalty-freeandfilter-freeapproachforsolvingnonlinearconstrainedoptimizationproblemshasbeenproposed.Byleveraginganadaptivesamplingstrategywithinadirectsearchframework,theapproachavoidsthecomplexityandcomputationalburdenassociatedwithpenaltyfunctionsandfilters.Numerica