基于量子遗传算法的滤波器参数优化

针对传统模拟滤波器设计对于较为复杂的目标需求往往精度与效率均较差的问题,提出一种基于量子遗传算法(quantum genetic algorithm,QGA)的模拟滤波器优化设计方法。量子遗传算法是量子计算理论与进化理论相结合的产物,同传统遗传算法(classical genetic algorithm,CGA)相比具有种群多样性好、收敛速度快和全局寻优能力强的特点。引入QGA算法对滤波器参数进行寻优。通过采用自适应的量子旋转角调整策略并引入量子交叉、变异及群体灾变操作,提高了算法的搜索效率,降低了算法出现早熟的可能性。实例计算表明了算法在该类问题中的有效性和可行性。

Optimization of filter parameters based on quantum genetic algorithm

The traditional design scheme of analog filters is imprecise and inefficient for complicated functional requirement.An optimization scheme of the analog filter design based on the quantum genetic algorithm(QGA) is proposed.Combined quantum theory with evolutionary theory,the QGA has better diversity than the classical genetic algorithm(CGA).Rapid convergence and good global search capacity characterize the performance of QGA.The optimization result can be obtained by the introducing of the QGA algorithm.With adopting an adaptive search grid adjustment strategy and quantum crossover,mutation,catastrophe operator,the efficiency of the proposed algorithm is enhanced and the possibility of prematurity is dropped effectively.The practical example shows the algorithm is effective and feasible.