Abstract
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.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant No. 51879118, in part by the High-Level Talent Training Project in the Transportation Industry under Grant No. 2019-014, in part by the National Natural Science Foundation of Fujian Province under Grant No. 2020J01688, in part by the Fujian Province Office of Science and Technology Support for Army under Grant No. B19101, in part by the Foundation of Fujian Education Committee of China for New Century Distinguished Scholars under Grant No. B17159, in part by the Scientific Research Foundation of the Key Laboratory of Fishery Equipment and Engineering at Ministry of Agriculture of the People’s Republic of China under Grant Nos. 2016002 and 2018001, in part by the Scientific Research Foundation of the Artificial Intelligence Key Laboratory of Sichuan Province under Grant No. 2017RYJ02, and in part by the Scientific Research Foundation of the Jiangsu Key Laboratory of Power Transmission & Distribution Equipment Technology under Grant No. 2017JSSPD01.
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Wang, R. Blind Source Separation Based on Adaptive Artificial Bee Colony Optimization and Kurtosis. Circuits Syst Signal Process 40, 3338–3354 (2021). https://doi.org/10.1007/s00034-020-01621-5
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DOI: https://doi.org/10.1007/s00034-020-01621-5