基于IQGA和SR的微弱信号检测方法研究

量子遗传算法(Quantum Genetic Algorithm, QGA)是量子计算与遗传算法相结合的产物，避免了遗传算法的一些缺陷。但QGA的编码方案和演化策略不具备通用性，因此极易陷入局部最优。针对QGA的上述缺陷，提出了一种在量子门更新的过程中，加入量子的交叉和变异操作的改进的QGA（Improved QGA, IQGA），且适当的改变旋转角的大小，保证收敛速度的同时提高了精度。随机共振（Stochastic Resonance, SR）是常用的微弱信号检测方法之一，将二者相结合，利用IQGA优化SR系统参数，实现SR最优输出的自适应求解。仿真结果表明，改进的量子遗传算法收敛速度快、寻优能力强。将该方法应用于工程实际，亦取得良好的效果。

Stochastic Resonance Based on IQGA in Weak Signal Detection

 Quantum genetic algorithm (QGA) combines the quantum and genetic algorithm, avoiding some shortcoming of genetic algorithm. But QGA is easily to fall into local optimum while its coding scheme and evolution of policy does not have the versatility. In this paper, the improved QGA (IQGA) with joining crossover and mutation in the process of quantum gate updating, is proposed. Meanwhile the size of rotation angle is changed appropriately to ensure both convergence speed and accuracy. Stochastic resonance (SR) is one of the applications in weak signal detection. IQGA is used to optimize the system parameters of SR to realize the adaptive solving of optimal output of SR. The simulation shows that the improved QGA has advantages of fast convergence speed and strong optimization ability. When applied in engineering practice, this method also achieves good results.