集美大学学报（自然科学版）2024，Vol.29Issue(1)：64-77,14.DOI:10.19715/j.jmuzr.2024.01.09
量子人工蜂群优化的盲源分离算法
Blind Source Separation Algorithm Based on Quantum Artificial Bee Colony Optimization
摘要
Abstract
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.关键词
盲源分离/量子人工蜂群算法/峰度/超高斯分布/亚高斯分布Key words
blind source separation/quantum artificial bee colony optimization/kurtosis/super-Gaussian distribution/sub-Gaussian distribution分类
信息技术与安全科学引用本文复制引用
程静,王荣杰..量子人工蜂群优化的盲源分离算法[J].集美大学学报（自然科学版）,2024,29(1):64-77,14.基金项目
国家自然科学基金项目(51879118) （51879118）
