Blind Source Separation Algorithm Based on Quantum Artificial Bee Colony Optimization
In order to achieve the separation of source signals subject to arbitrary distribution,an improved quantum artificial bee colony method was proposed for optimizing the blind source separation algorithm.First,on the basis of the standard quantum artificial bee colony algorithm,a chaotic optimization operator was intro-duced to generate the initial solution,so that the solutions of the initial population were uniformly distributed on the feasible solution space;Second,dynamic neighborhood factor and forgetting factor were introduced in the search stage to control the optimization direction,improving the convergence speed and optimization ability;Finally,the objective function was constructed based on signal kurtosis,and the separation matrix was obtained by optimizing the objective function using the improved quantum artificial bee colony method and hence one could realize the separation of mixed signals.The simulation results showed that the proposed algorithm was able to separate sub-Gaussian distribution,super-Gaussian signal and the mixed signal of both,and it outper-forms the traditional algorithm in terms of convergence speed and separation accuracy.
blind source separationquantum artificial bee colony optimizationkurtosissuper-Gaussian distributionsub-Gaussian distribution
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