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1、AbstractIIIAbstractEvolutionaryAlgorithmprovidesanewwaytosolvecomplexoptimizationproblems.Becauseofitsintelligence,universality,robustnessandglobalsearchability,EAshavebeenwidelyusedinthisfieldandhaveagreatsuccessinrecentlyseveraldecades.Itiscommontofaceanumberofoptimization
2、problemsinmanyareasoftherealworld,especiallyinthescienceandengineeringfields.However,theseproblemsareoftenconstrained.Becauseofthedifferentfeaturesoftheseproblems,thetraditionalmethodsarehardtosolvetheseproblemseffectively.Asroustpopulation-basedglobalsearchmethods,Evolution
3、aryAlgorithms(EAs)areverypromisingtosolvetheconstrainedoptimizationproblems.TheaimofthisdissertationistoexplorethetheoriesandmechanismsofEAs,andtodothecorrespondingtheoreticandexperimentalanalyses.Themainresearchworkinthisdissertationconsistofthefollowingaspects.(1)Weextendt
4、hemultiagentgeneticalgorithm(MAGA)tosolveconstrainedoptimizationproblems(COPs)(MAGA_COPs)bycombiningtheneighborhoodcompetitionoperatorwithanefficientconstrainthandlingtechnique.Thismethodcanmakegooduseoftheinformationofinfeasiblesolutionswhichisaimatguidingthesearchtowardthe
5、globaloptimaofCOPs.Thisalgorithmistestedon12benchmarkfunctions,andtheresultshowsthat12benchmarkfunctionscanfindglobaloptima.(2)AnapproximationstrategyforfeasibleregionalsousedinMAGA_COPs,thismethodmakethesolutionoffunctionstoapproachtheglobaloptimalsolutionandeffectiveinprev
6、entingthealgorithmtrappingintolocaloptima.Thealgorithmistestedon12benchmarkfunctions,andtheresultshowsthatthealgorithmisoutperformsotherscomparedwithsomeotherstate-of-the-artalgorithms.(3)WeimproveMAGA_COPsbycombiningMAGA_COPswithtraditionalmethods.Hybridapproachbasedonmulti
7、agentgeneticalgorithmisgiven,inordertoovercometheslowerconvergenceofthestandardgeneticalgorithm,andinwhichthelocalsearchisweak.Thealgorithmistestedon12benchmarkfunctions,andtheresultshowsthatthealgorithmisanefficientandconvergenthybridgeneticalgorithm.(4)Weimprovethemultiage
8、ntgeneticalgorithm(MAGA)tosolvelayoutoptimizationbycombiningtheneighborhood
