【摘要】 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!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。更多还原

【Abstract】 This paper first proposes a novel quantum-inspired multi-objective evolutionary algorithm which 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 -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 the new approach outperforms the state-of-art MOEA SPEA2.更多还原