An Efficient Framework Using Normalized Dominance Operator for Multi-Objective Evolutionary Algorithms

Multi-objective optimization algorithms using evolutionary optimization methods have shown strengthinsolvingvariousproblemsusingseveraltechniquesforproducinguniformlydistributed setofsolutions.Inthisarticle,aframeworkispresentedtosolvethemulti-objectiveoptimization problemwhichimplementsanovelnormalizeddominanceoperator(ND)withtheParetodominance concept.Theproposedmethodhasalessercomputationalcostascomparedtocrowding-distance-based algorithmsandbetterconvergence.Aparallelexternalelitistarchiveisusedwhichenhancesspread ofsolutionsacrosstheParetofront.Theproposedalgorithmisappliedtoanumberofbenchmark multi-objectivetestproblemswithupto10objectivesandcomparedwithwidelyacceptedaggregationbasedtechniques.Experimentsproduceaconsistentlygoodperformancewhenappliedtodifferent recombinationoperators.Resultshavefurtherbeencomparedwithotherestablishedmethodstoprove effectiveconvergenceandscalability. KEywORDS Elitist Archive, Evolutionary Algorithm, Genetic Algorithm, Multi-Objective Optimization, Normalized Dominance Operator

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