一种改进的双链量子遗传算法及其应用

Improved quantum genetic algorithm with double chains and its application

针对目前双链量子遗传算法中保持种群多样性和改善优化效率问题提出了三种改进方法。通过在量子比特概率幅三角函数表达式中引入常数因子,使搜索过程在多个周期上同时进行,以改善算法的优化效率;提出了一种基于单比特量子Hadamard的变异策略,可提高保持种群多样性的概率;改进了量子旋转门转角步长函数,能够有效避免算法震荡,增强算法的适应性。以多变量函数极值优化问题为例,仿真实验结果表明上述三种改进措施是有效的。

Aiming at the problems that how to keep population diversity and improve optimization efficiency in double chains quantum genetic algorithm, this paper proposed three improvements. Firstly, by adding the constant factor to the trigonometric expressions of quantum bit probability amplitudes, performed the search in a number of trigonometric functions cycle at the same time, which enhanced the optimization efficiency of the proposed algorithm. Secondly, the mutation strategy applying the single bit quantum Hadamard gates enhanced the diversity of population. Thirdly, enhanced the adaptability of the proposed algorithm by redesigning the step function of rotation angle of quantum rotation gates, and this also avoided the oscillation effectively. Finally, with application of function extremum optimization with multi-variables, the simulation results show that the three improvements are efficient.