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Niche Pseudo-Parallel Genetic algorithms for Path Optimization of Autonomous Mobile Robot

JournalofShanghaiUniversity(EnglishEdition),2006,10(5):449-453ArticleID:100726417(2006)0520449205

Nichepseudo2parallelgeneticalgorithmsforpathoptimizationofautonomousmobilerobot

SHENZhi2hua(沈志华)

1,2

, ZHAOYing2kai(赵英凯)

1,3

, WUWei2wei(吴炜炜)

2

1.CollegeofAutomation,NanjingUniversityofTechnology,Nanjing210009,P.R.China

2.DepartmentofElectricalandElectronicEngineering,UniversityofMelbourne,Victoria3010,Australia3.ResearchInstituteofAutomation,NanjingUniversityofTechnology,Nanjing210009,P.R.China

Abstract Anewgeneticalgorithmnamednichepseudo2parallelgeneticalgorithm(NPPGA)ispresentedforpathevolutionandgeneticoptimizationofautonomousmobilerobot.TheNPPGAisaneffectiveimprovementtomaintainthepopulationdiversityaswellforthesakeofavoidingprematureandstrengthenparallelismofthepopulationtoacceleratethesearchprocesscombinedwithnichegeneticalgorithmsandpseudo2parallelgeneticalgorithms.Theproposedapproachisevaluatedbyroboticpath,whichisaspecificapplicationoftravelersalesmanproblem(TSP).Experimentalresultsindicatedabeobtainedinthepracticaltravelingsalesmanproblemnamed“RobottouraroundPekin”,andthethansimplegeneticalgorithm(SGA)anddistributedparalellgeneticalgorithms(DPGA).

Keywords geneticalgorithms,travelersalesmanTSP,,pseudo2parallel.

1 I Pathplanningofmobilerobotispivotal

techniqueformachineintelligence,whichaimstofindanon2collisionpathfrominitialpositiontoobjectivepositionaccordingtoevaluationfunctionsinan

[1]

obstaclespace.Itcanbedescribedastravelersalesmanproblem(TSP),atypicalcombinationoptimizationproblem,whichbelongstothewell2

[2]

knownNP2hardoptimization.ThemathematicaldefinitioncanberegardedasamapG=(V,E),whereeachlinee∈Ehasanonnegativepowerω(e).TheaimistofindaHamiltoncirclenotedwithCofmapGinordertoobtainaminimumpowerW(C)=∑ω(e).

e∈E(C)

Geneticalgorithmisnumericaloptimizationmethodsbasedonthetheoryofgeneticsandnaturalselection.Itisagenerallyprobabilisticandadaptableconceptforproblemsolving,especiallysuitableforsolvingdifficultyoptimizationandevolutionproblems,wheretraditionalmethodsarelessefficient. Anadvancedgeneticalgorithm,nichepseudo2parallelgeneticalgorithm(NPPGA)ispresentedbasedonsimplegeneticalgorithm(SGA),nichegeneticalgorithm(NGA)andparallelgeneticalgorithm(PGA)tofurtherimprovetheGAforrobotpathoptimization.ResearchonNPPGAisavailableforlotsofpracticalproblemssuchaspathroutingoptimization,netsorganization,jobdistribution,schedulingoptimization,etc.

[4]

2 Mechanism

 Sometraditionalmethodssuchasgreedarithmetic,

vicinagearithmeticanddynamicprogramming

[3]

algorithmdonotprovidegoodperformanceoncombinationexplosionarousedbyrapidincrementinexponentwithinasolutionsetofmathematicmodel,alsoknownastheveryquickaccretioninbothaspectsofspaceandtimecomplicationalongwiththeincreaseofdegrees.Averypromisingdirectionisthegeneticalgorithm(GA)exceptforthetraditionalmethods.

ReceivedNov.18,2004; RevisedNov.25,2005ZHAOYing2kai,Prof.,E2mail:zhaoyk@http://doc.xuehai.net

ofnichepseudo2parallel

geneticalgorithms

 ThefoundationofNPPGAisgeneticalgorithm,

whichisaclassofglobal,adaptable,andprobabilisticsearchoptimizationandrevolutionalgorithmgleanedfromthemodeloforganicevolutionandalsosimulatesthegeneticsandevolutionofbiologicpopulationinnature.GAadoptsnaturallyevolutionarymodelsuchasselection,crossover,mutation,deletionandtransference.Mathematically,thisevolutionaryprocessisatypicalalgorithmtofindouttheoptimalsolutionviaiterationsearchamongmulti2elementin

Niche Pseudo-Parallel Genetic algorithms for Path Optimization of Autonomous Mobile Robot

a

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