A novel technique is proposed for selecting iterative updates and step sizes based on adaptive function values to compensate for the slow convergence rate of artificial bee colony optimization (ABCO). On this basis, a blind source separation (BSS) algorithm is proposed based on adaptive ABCO and kurtosis, which does not impose any hypothetical requirements on the source signal. By using kurtosis as the objective function, the algorithm can separate signals that follow any distribution. BSS results from various test distributions demonstrate the superior performance of the proposed algorithm compared to conventional methods.

中文翻译：

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基于自适应人工蜂群优化和峰度的盲源分离

提出了一种新技术，用于基于自适应函数值选择迭代更新和步长，以补偿人工蜂群优化（ABCO）的缓慢收敛速度。在此基础上，提出了一种基于自适应ABCO和峰度的盲源分离（BSS）算法，该算法对源信号没有任何假设要求。通过使用峰度作为目标函数，该算法可以分离遵循任何分布的信号。来自各种测试分布的BSS结果证明，与传统方法相比，该算法具有更高的性能。