Multi-objectiveFMSprocessplanningwithvariousflexibilitiesusing a symbioticevolutionaryalgorithm

This paperpresentsanevolutionaryalgorithm,calledthemulti-objectivesymbioticevolutionary algorithm(MOSEA),tosolveamulti-objectiveFMSprocessplanning(MFPP)problemwithvarious flexibilities.TheMFPPproblemsimultaneouslyconsidersfourtypesofflexibilitiesrelatedtomachine, tool, sequence,andprocessandtakesintoaccountthreeobjectives:balancingthemachineworkload, minimizingpartmovements,andminimizingtoolchanges.TheMOSEAismodeledasatwo-leveled structuretofindasetofwell-distributedsolutionsclosetothetrueParetooptimalsolutions.To promotethesearchcapabilityofsuchsolutions,twomainprocessesimitatingsymbioticevolutionand endosymbioticevolutionareintroduced,togetherwithanelitiststrategyandafitnesssharingscheme. EvolutionarycomponentssuitablefortheMFPPproblemareprovided.Withavarietyoftest-bed problems,theperformanceoftheproposedMOSEAiscomparedwiththoseofexistingmulti-objective evolutionaryalgorithms.TheexperimentalresultsshowthattheMOSEAispromisinginsolution convergenceanddiversity.