Improved Quantum Genetic Algorithm With Double Chains And Its Application In Signal Denoising

As a new type of intelligent optimization algorithms,quantum genetic algorithm has been widely used in the fields of communication encryption,signal processing,artificial intelligence.Although the development speed of quantum genetic algorithm is very fast,the research is still at an early stage because of a relatively short time.In the design of existing quantum genetic algorithm,there are also many defects,such as large coding space,unreasonable revolving gate angle adjustment and invalid quantum mutation,which lead to the slow computing efficiency,low accuracy and poor robustness.These defects restricted the development of the quantum genetic algorithm.so that the quantum genetic algorithm should be improved to create a stable and reliable mathematical model,which can exert its advantages in more fields.To solve the problems of slow convergence speed,low search precision and poor robustness in traditional double chains quantum genetic algorithm,a new double chains quantum genetic algorithm(F_DCQGA)with the characteristic of high search density,adaptive step-length updating is suggested.Firstly,the coding space is mapped to reduce the algorithm searching space and increases searching density,under the premise of guaranteeing quantum population adaptation and argument population monotonicity.Secondly,the adaptive step-length factor is introduced to the quantum updating,which changes the step-length with gradient of objective function in searching points.This could solve the global optimal solution search difficulties caused by oscillatory occurrence in traditional optimization algorithm.Finally,quantum new gate is presented in chromosome mutation updating,to improve the shortcoming that NOT gate can not update quantum bit probability amplitude.Additionally,the F_DCQGA is applied to the wavelet threshold denoising and selection of threshold function mechanism.Simulation results show that the wavelet threshold denoising of F_DCQGA has better denoising effect.In signal denoising,the smaller mean square error(MSE)and larger peak signal to noise ratio(PSNR)are gained.Simultaneously,the high frequency information is also retained.F_DCQGA improves the convergence speed and searching precision of the wavelet threshold and threshold selection mechanism function.It also has very high practical application value.