摘要： 量子遗传算法易陷入局部极值。为此，提出一种改进量子旋转门的量子遗传算法。将量子比特的概率幅值应用于染色体编码，使用量子旋转门实现染色体的更新操作，从而实现目标的优化求解。理论分析及实验结果表明，该算法以概率1收敛，强收敛于1?ε，与双链遗传算法相比，能增加算法复杂度，延长平均时间，对验证函数1收敛次数由3次增加到7次，对验证函数2收敛次数由8次增加到9次。

关键词: 量子比特, 量子遗传算法, 量子染色体, H?门, 收敛性

Abstract: Aiming at the problems that the Quantum Genetic Algorithm(QGA) easily falls into local extremum, an improved QGA algorithm of quantum revolving gate is proposed. In order to achieve the goal of optimization solving, quantum bit probability amplitude is applied to the chromosome encoding, and quantum rotation gate is used to chromosome updating. The theoretical derivation proves that the proposed algorithm converges with probability 1, and the algorithm converges strongly to 1?ε. Comparing with double QGA, simulation results show that the algorithm increases complexity, the average time extension, but convergence times increase, convergence times of the function 1 increases from 3 to 7, and convergence times of the function 2 increases from 8 to 9.

Key words: quantum bit, Quantum Genetic Algorithm(QGA), quantum chromosome, H? gate, convergence

中图分类号: 

• TP301.6