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Based on the inspiration of immune system, a new multi-objective optimization algorithm is presented. The proposed approach adopts a cluster mechanism in order to divide the population into subpopulations for the stage of selection and reproduction. In the immune clonal selection process, a hybrid hypermutation operator is introduced to improve the variety of antibodies and affinity maturation, thus it can quickly obtain the global and local optima. The simulation results illustrated that the efficiency of the proposed algorithm for complicated function optimization and verified it's remarkable quality of the global and local convergence reliability.