DMQEA: Dual Multiobjective Quantum-inspired Evolutionary Algorithm

This paper proposes dual multiobjective quantum-inspired evolutionary algorithm (DMQEA) with the dualstage of dominance check by introducing secondary objectives in addition to primary objectives. The secondary objectives are to maximize global evaluation values and crowding distances of the solutions in the external global population obtained for the primary objectives and the previous archive obtained from the secondary objectives-based nondominated sorting. By employing the secondary objectives for sorting the solutions in each generation, DMQEA can induce the balanced exploration of the solutions in terms of user’s preference and diversity to generate preferable and diverse nondominated solutions in the archive. To demonstrate the effectiveness of the proposed DMQEA, empirical comparisons with MQEA, MQEA-PS, and NSGA-II are carried out for benchmark functions.