## 5 Conclusions  
In this paper, we propose a blind source separation algorithm based on BAS-BCA, which is optimized by the BAS algorithm and then combined with the BCA algorithm for blind source separation, thus enhancing the universality, as well as the accuracy of the blind source separation algorithm while having better operation efficiency and separation accuracy. The algorithm introduces a step decay factor in BAS to avoid falling into the local optimum and improve the convergence performance and then applies the improved BAS algorithm to the optimization process of the BCA algorithm to make the whole algorithm have higher operation efficiency and better optimization accuracy. The constraints of the prior assumptions of this algorithm are more relaxed, and there is no need to ensure that the source signals are independent of each other, so it can cope with more complex environments and more diverse source signals.  
In order to verify the effectiveness and superiority of the present algorithm, simulation, and analysis experiments are conducted. First, the separation simulation of independent sources was performed, and the similarity coefficients of their separated signals and source signals were 0.97990255, 0.96984297, and 0.94597251, which were all close to 1, proving that the present algorithm is suitable for blind source separation of independent sources as other blind source separation algorithms. Then, the simulation of blind source separation was carried out for dependent signal sources, and the separation effects were compared with those of FastICA and SCA, two traditional blind source separation algorithms, and the BCA algorithm. This algorithm is also applicable to the separation of dependent source signals and has better separation accuracy and running efficiency than the other three algorithms, as evidenced by the similarity coefficient, performance index (PI), running time, and signal interference ratio (SIR) of the separated signals. We concluded that the lower the independence of the source signal, the worse the effect of the traditional blind source separation algorithm. Finally, this algorithm was applied to the blind source separation of images and compared with the other three algorithms, which shows that the performance of this algorithm in image separation also has a better separation effect.  
[PERSON] performed manuscript writing, data collection, code writing, and experimental analysis. [PERSON] conceived and supervised the project. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Major Science and Technology Project (J2019-V-0010-0105) and the Fundamental Research Funds for the Central Universities (G2021 KY05122).  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **FastICA** & **SCA** & **BCA** & **BAS-BCA** \\ \hline
**similarity** & 0.81583695 & 0.85426875 & 0.96189537 & 0.98256749 \\
**coefficient** & 0.80563229 & 0.82156371 & 0.92678932 & 0.96585374 \\  & 0.83652718 & 0.84685932 & 0.95345874 & 0.99136457 \\
**PI** & 0.29356417 & 0.27998659 & 0.14567789 & 0.11268894 \\
**SIR** & 18.1235845 & 20.2314567 & 232589671 & 28.9358791 \\
**Time** & 21.56 & 24.13 & 33.15 & 28.35 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Data of separation performance evaluation index.