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《畢業(yè)設(shè)計(jì)（論文）-求解非線性規(guī)劃問題的遺傳算法設(shè)計(jì)與實(shí)現(xiàn)》由會員上傳分享，免費(fèi)在線閱讀，更多相關(guān)內(nèi)容在學(xué)術(shù)論文-天天文庫。
1、摘要非線性規(guī)劃在工程、管理、經(jīng)濟(jì)、科研、軍事等方面都有廣泛的應(yīng)用。傳統(tǒng)的解決非線性規(guī)劃問題的方法，如梯度法、罰函數(shù)法、拉格朗日乘子法等，穩(wěn)定性差，對函數(shù)初值和函數(shù)性態(tài)要求較高，且容易陷入局部最優(yōu)解。遺傳算法是模擬達(dá)爾文的遺傳選擇和自然淘汰的生物進(jìn)化過程的計(jì)算模型。遺傳算法是一種全局搜索算法，簡單、通用、魯棒性強(qiáng)，對目標(biāo)函數(shù)既不要求連續(xù)，也不要求可導(dǎo)，適用于并行分布處理，應(yīng)用范圍廣。本文在分析傳統(tǒng)的非線性規(guī)劃算法的不足和遺傳算法的優(yōu)越性的基礎(chǔ)上，將遺傳算法應(yīng)用于非線性規(guī)劃。算法引進(jìn)懲罰函數(shù)的概念，構(gòu)造帶有懲罰項(xiàng)的適應(yīng)度函數(shù)；通過實(shí)數(shù)編碼，轉(zhuǎn)輪法選擇，雙點(diǎn)交叉，均
2、勻變異，形成了求解非線性規(guī)劃問題的遺傳算法。與傳統(tǒng)的非線性規(guī)劃算法——外點(diǎn)罰函數(shù)法的比較結(jié)果表明該算法在一定程度上有效地克服了傳統(tǒng)的非線性規(guī)劃算法穩(wěn)定性差，對函數(shù)初值和函數(shù)性態(tài)要求較高，且容易陷入局部最優(yōu)解的缺陷，收斂更合理，性能更穩(wěn)定。關(guān)鍵詞：非線性規(guī)劃；遺傳算法；罰函數(shù)法ABSTRACTNon-linearprogramminghasawiderangeofapplicationsinengineering,management,economic,scientific,andmilitaryaspects.Traditionalmethodstosolve
3、thenon-linearprogrammingproblem,suchasthegradientmethod,penaltymethod,Lagrangemultipliermethod,havepoorstability.Theyaresensitivetothefunctioninitialvalueandrequesttheobjectivefunctiontobecontinuousanddifferential.Theresultsarealsoeasilytrappedintolocaloptimalsolution.Geneticalgorit
4、hmisakindofcalculatemodelwhichsimulatesDarwin'sgeneticselectionandbiologicalevolutionofnaturalselection.Geneticalgorithmisaglobalsearchalgorithm.Ithassimple,universal,robustfeatures,anddoesnotrequesttheobjectivefunctiontobecontinuousanddifferential,andissuitableinparalleldistributio
5、nprocessing.Geneticalgorithmiswidelyappliedinmanyareas.Basedontheanalysisofthedisadvantageoftraditionalnon-linearprogrammingalgorithmandtheadvantageofgeneticalgorithm,geneticalgorithmisappliedtonon-linearprogramminginthispaper.Theintroductionoftheconceptofpenaltyfunctionisusedtocons
6、tructthefitnessfunctionwithpunishment.Byusingreal-coded,RouletteWheelselectionmethod,two-pointcrossover,uniformmutation,weformedageneticalgorithmtosolvethenon-linearprogrammingproblem.Comparedwiththemostclassicalandwidelyusedtraditionalnon-linearprogrammingproblemalgorithm–SUMTalgor
7、ithm,theresultsshowthatthenewalgorithmcouldeffectivelyovercomethedefectofthetraditionalalgorithminacertainextent.Thenewalgorithmismorestable,lesssensitivetothefunctioninitialvalueandconditions,andalwayscouldreceivetheoptimalsolutionorapproximateoptimalsolution.Itsconvergenceresultsa
8、remorereasonable,th
