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1、AnewtransformationintoStateTransitionAlgorithmforfindingtheglobalminimumXiaojunZhou,ChunhuaYangandWeihuaGuiAbstract—Topromotetheglobalsearchabilityoftheoriginalwherexkstandsforastate,correspondingtoasolutionofstatetransitionalgorithm,anewoperatorcalledaxes
2、ionistheoptimizationproblem;AkandBkarestatetransitionsuggested,whichaimstosearchalongtheaxesandstrengthenmatrixes,whichareusuallytransformationoperators;ukissingledimensionalsearch.Severalbenchmarkminimizationproblemsareusedtoillustratetheadvantagesofthei
3、mprovedthefunctionwithvariablesxkandhistorystates;fisthealgorithmoverotherrandomsearchmethods.Theresultsofobjectivefunctionorevaluationfunction.numericalexperimentsshowthatthenewtransformationcanUsingvarioustypesofspacetransformationforreference,enhanceth
4、eperformanceofthestatetransitionalgorithmandthreespecialstatetransformationoperatorsaredefinedtothenewstrategyiseffectiveandreliable.solvecontinuousfunctionoptimizationproblems.I.INTRODUCTION(1)RotationtransformationHEbasicrandomoptimization(BRO)waspropose
5、dby1xk+1=xk+αRrxk(2)TMatyas[1]in1965,anditisprovedthattheBROcannkxkk2ensuresconvergencetoaglobalminimumwithprobabilityn×1wherexk∈ℜ,αisapositiveconstant,calledrotationone([2],[3]).Toenhancetheperformanceoftherandomn×nfactor;Rr∈ℜ,israndommatrixwithitselemen
6、tsoptimization,variousstrategieshavebeenintroduced.Inbelongingtotherangeof[-1,1]andk·k2is2-normofa[4],toadjusttheparametersofmeanandstandarddevia-vector.IthasprovedthattherotationtransformationhasthetioninaGaussianvector,aheuristicrandomoptimizationfuncti
7、onofsearchinginahypersphere[8].(HRO)warpresented,utilizingtwodifferentmechanisms(2)Translationtransformationbasedongradientinformationandreinforcement,respec-tively.In([5],[6]),twoapproachesnamedadaptiverandomxk−xk−1xk+1=xk+βRt(3)searchtechnique(ARSET)and
8、dynamicrandomsearchtech-kxk−xk−1k2nique(DARSET)wereputforwardbyCoskunHamzacebiwhereβisapositiveconstant,calledtranslationfactor;RtandFevziKutay,tofacilitatethedeterminationoftheglobal1∈ℜisarandomvariablewithitseleme
