Computer Engineering and Applications ›› 2017, Vol. 53 ›› Issue (18): 204-207.DOI: 10.3778/j.issn.1002-8331.1603-0377

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Independent component analysis based on improved artificial bee colony algorithm

ZHAO Liquan, WANG Xin   

  1. College of Information Engineering, Northeast Dianli University, Jilin, Jilin 132012, China
  • Online:2017-09-15 Published:2017-09-29


赵立权,王  欣   

  1. 东北电力大学 信息工程学院,吉林省 吉林市 132012

Abstract: For the fast fixed point independent component analysis method is easy to fall into local optimal solution, an improved ICA method based on artificial bee colony algorithm is proposed. The algorithm uses the absolute value of kurtosis as the optimized objective function, improves the following and scouts stage searches equation of artificial bee colony algorithm and optimizates the independent component analysis. At the onlookers stage, the new candidate positions are generated near the optimal solution in the current iteration, to avoid weakening the exploitation ability with random search. At the scouts phase, the distance between the optimal and the worst solutions of the current iteration is utilized to replace the worst solution in the process of optimization, so as to improve the optimization effect of artificial bee colony optimization method and the accuracy of independent component analysis. The experiment confirms the effectiveness of the improved algorithm.

Key words: Independent Component Analysis(ICA), artificial bee colony, kurtosis, accuracy

摘要: 针对快速固定点独立分量分析方法容易陷入局部最优解的问题,提出了一种基于改进的蜂群优化的独立分量分析方法。该方法以信号的峭度作为代价函数,利用人工蜂群方法对其进行优化。在优化的过程中,一方面为了避免随机搜索造成的开采能力弱的问题,在跟随蜂搜索阶段采用当前迭代最优解引导的方式产生新的候选解,另一方面,为了避免产生更差的解,在侦查蜂阶段,利用当前迭代中的最优解与最差解的距离产生新的解,代替最差解,提高人工蜂群优化方法的寻优效果,进而提高独立分量分析的精度。实验仿真验证了算法的性能。

关键词: 独立分量分析, 人工蜂群, 峭度, 精度