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ID:32069539
大小:2.33 MB
页数:60页
时间:2019-01-31
《群体智能优化算法在精密工程计算中的应用》由会员上传分享,免费在线阅读,更多相关内容在学术论文-天天文库。
1、AbstractAbstractMostoftheintelligenceoptimizationalgorithmswereproposedandquicklythdevelopedinthe20century.Theyhaveprovidedausefultooltosolvethecomplexoptimizationproblems.Howeverbecauseofthevarietyofalgorithmsandcomplexityofproblemstosolve,itisstillahottopicof
2、howtoadoptasuitablealgorithmtosolveanoptimizationproblem.Aimingatthenonlinearissuesintheprecisionmechanicalengineering,twooptimizationalgorithmshavebeenstudiedindetailsincludingGeneticAlgorithm,GA(ofwhichagroupofindividualstakepartintheoptimization)andParticleS
3、warmOptimization,PSO.SomeoptimizationproblemsinprecisionmechanicalengineeringhavebeensolvedusingGAandPSObasedontheresearchoftheirprinciplesandimplementationtechniques.Thecontentsincludedthat:(1)firstthepresentresearchbackgroundisreviewedandthetheoriesofGAandPSO
4、arediscussedandtheirimplementationtechniquesaretalkedandcompared;(2)afewproblemsareanalyzedfromthemechanicalengineeringsuchasstructuresdesign,geometricalerrorevaluationandscanningcontrol,ofwhichtheobjectivefunctionsandvariablesarededucedforcomputationusingoptim
5、izationalgorithms;(3)finallysomeexamplesareselectedtoverifiedthemethods.Thetheoreticalanalysisandexperimentalverificationmethodsareadoptedintheresearch.Inthestudy,animprovedimagesgrayvariancefunctionisutilizedtodeterminationofautofocusinginthescanningwhitelight
6、interferometry.AndanadaptiveweightduringtwoperiodswasadoptedinthePSOalgorithm.Theresearchworkwasfulfilledbyusinganeasyunderstandinglanguagetoanalyzethebasicprinciplesandimplementationtechniquesoftwooptimizationalgorithms.Sometypicalexampleswerechosetobesolvedby
7、thetechniques,whichplaysacriticallyimportantroleintheoriesandpractice.KeyWords:SwarmIntelligenceGeneticAlgorithm(GA)ParticleSwarmIII华侨大学硕士学位论文Optimization(PSO)MechanicalEngineeringOptimizationProblemIV目录目录第1章引言...................................................
8、..............................................................11.1课题的提出和选题背景...................................................................................11.2群体智能优化算法国内
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