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Underdetermined blind source separation method based on quantum Archimedes optimization algorithm

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Abstract

The performance of the existing underdetermined blind source separation methods is very sensitive to the initial parameters, meanwhile, the existing setting or selection methods of initial parameters need to be improved. Consequently, an effective underdetermined blind source separation method is proposed in this paper to solve the above engineering problems. Based on the Archimedes optimization algorithm and quantum computing theory, this paper proposes a novel intelligent algorithm named quantum Archimedes optimization algorithm, which solves the objective functions for engineering problems. Then the optimal solution obtained through the quantum Archimedes optimization algorithm is used as the initial clustering centers of the K-means clustering algorithm to achieve mixing matrix estimation. In addition, the original initial estimation signal setting of the source recovery based on radial basis function network is converted into an initial solution in population for quantum Archimedes optimization algorithm. The optimal solution obtained through the quantum Archimedes optimization algorithm is used as the new initial estimation signal setting to achieve source recovery. The simulation results show that the proposed underdetermined blind source separation method has higher accuracy than previous methods. The proposed method that is more robust and applicable makes the setting and selection of initial parameters more reasonable so that the performance is no longer limited to the initial parameters.

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Data Availability

The data used to support the findings of this study are included within the article. The source codes of QAOA are publicly available at https://github.com/heuzzw/QAOA-matlab-code.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (no.61571149), the Natural Science Foundation of Heilongjiang Province (no.LH2020F017), and the Initiation Fund for Postdoctoral Research in Heilongjiang Province (no.LBH-Q19098).

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  1. College of Information and Communication Engineering, Harbin Engineering University, Harbin, 150001, China

    Hongyuan Gao, Zhiwei Zhang, Shihao Wang & Helin Sun

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Zhiwei Zhang, Shihao Wang and Helin Sun are contributed equally to this work.

Appendix A Test functions

Appendix A Test functions

Table 9 30 test functions for experiments
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Gao, H., Zhang, Z., Wang, S. et al. Underdetermined blind source separation method based on quantum Archimedes optimization algorithm. Appl Intell 53, 13763–13800 (2023). https://doi.org/10.1007/s10489-022-03962-x

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