A novelcompetitiveco-evolutionaryquantumgeneticalgorithmforstochasticjob shop schedulingproblem

In thispaper,anovelcompetitiveco-evolutionaryquantumgeneticalgorithm(CCQGA)isproposedfor a stochasticjobshopschedulingproblem(SJSSP)withtheobjectivetominimizetheexpectedvalueof makespan. Threenewstrategiesnamedascompetitivehunter,cooperativesurvivingandthebigfish eating smallfisharedevelopedinpopulationgrowthprocess.Basedonimprovedco-evolutionideaof multi-population andconceptsofquantumtheory,thisalgorithmcouldnotonlyadjustpopulationsize dynamically toincreasethediversityofgenesandavoidprematureconvergence,butalsoacceleratethe convergence speedwithQ-bitrepresentationandquantumrotationgate.FTbenchmark-basedproblems where theprocessingtimesaresubjectedtoindependentnormaldistributionsaresolvedeffectively by CCQGA.TheexperimentresultsachievedbyCCQGAarecomparedwithquantum-inspiredgenetic algorithm (QGA)andstandardgeneticalgorithm(GA),whichshowsthatCCQGAhasbetterfeasibilityand effectiveness.