下载本文档
版权说明:本文档由用户提供并上传,收益归属内容提供方,若内容存在侵权,请进行举报或认领
文档简介
一种求解非线性约束优化问题的无罚函数无滤子的方法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
温馨提示
- 1. 本站所有资源如无特殊说明,都需要本地电脑安装OFFICE2007和PDF阅读器。图纸软件为CAD,CAXA,PROE,UG,SolidWorks等.压缩文件请下载最新的WinRAR软件解压。
- 2. 本站的文档不包含任何第三方提供的附件图纸等,如果需要附件,请联系上传者。文件的所有权益归上传用户所有。
- 3. 本站RAR压缩包中若带图纸,网页内容里面会有图纸预览,若没有图纸预览就没有图纸。
- 4. 未经权益所有人同意不得将文件中的内容挪作商业或盈利用途。
- 5. 人人文库网仅提供信息存储空间,仅对用户上传内容的表现方式做保护处理,对用户上传分享的文档内容本身不做任何修改或编辑,并不能对任何下载内容负责。
- 6. 下载文件中如有侵权或不适当内容,请与我们联系,我们立即纠正。
- 7. 本站不保证下载资源的准确性、安全性和完整性, 同时也不承担用户因使用这些下载资源对自己和他人造成任何形式的伤害或损失。
最新文档
- 2023-2024学年黑龙江省大庆市肇源县六年级(上)期末语文试卷(五四学制)
- 2024年吉林省长春市绿园区中考语文一模试卷(含解析)
- 八年级数学上册试题 第11章 《平面直角坐标系》(单元测试提高卷)-沪科版(含答案)
- 橡胶沥青行业特点
- 食品工厂项目节能评估报告
- 2024届高考压轴卷(全国甲卷)语文试题
- 2024届河北省定兴中学高一数学第二学期期末学业质量监测试题含解析
- 2023-2024学年北京市大兴区高一数学第二学期期末调研试题含解析
- 2023读书时坚持的诗句或名言
- 2024届河南省驻马店市上蔡二高高一下数学期末学业质量监测试题含解析
- 康养项目咨询策划合作协议书
- 日间手术的麻醉
- 总监理工程师带班记录表
- 新教材人教版高中化学选择性必修三 3.5 有机合成 知识点梳理
- 临床试验伦理汇报PPT模板
- 《廉洁自律加强自身修养》银行新员工培训课件(37页PPT)
- 《在长江源头各拉丹冬》优质教案
- 微生物表面擦拭方法验证方案
- 钢网架结构安装施工方案(完整版)
- 工业园物业管理流程图
- 哈尔滨工业大学信纸模版
评论
0/150
提交评论