Abstract: This paper is the first to propose a novel quantum-inspired multi-objective evolutionary algorithm employs the theory of quantum computation to multi-objective optimization. A Q-bit chromosome representation is adopted, the quantum rotation gate strategy and quantum mutation are applied to evolve the population, the concept of the ε-dominance can help our algorithm maintain a sequence of well-spread solutions, and we introduce a new approach based on quick sort to construct non-dominated set, which can reduce the time complexity. It is shown by experiments that our new approach outperforms the state-of-art MOEA SPEA2

Key words: Multi-objective evolutionary algorithm, Quantum-inspired Multi-objective evolutionary algorithm, Multi-objective optimization

摘要： 本文首次将量子计算的理论用于多目标优化，提出量子多目标进化算法（QMOEA），其采用量子位染色体表示法，利用量子门旋转策略和量子变异实现群体的进化，使用ε支配关系构造外部种群以此保持算法的较好分布性，提出基于快速排序的非劣最优解构造方法加快算法运行效率，实验表明，这种方法与经典的多目标进化算法SPEA2相比，其收敛性更好且分布更均匀

关键词: 多目标进化算法, 量子多目标进化算法, 多目标优化