Blind Source Separation Based on Double-Mutant Butterfly Optimization Algorithm
The conventional blind source separation independent component analysis method has the problem of low-separation performance. In addition, the basic butterfly optimization algorithm has the problem of insufficient search capability. In order to solve the above problems, an independent component anal...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/11/3979 |
| _version_ | 1797491763329368064 |
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| author | Qingyu Xia Yuanming Ding Ran Zhang Minti Liu Huiting Zhang Xiaoqi Dong |
| author_facet | Qingyu Xia Yuanming Ding Ran Zhang Minti Liu Huiting Zhang Xiaoqi Dong |
| author_sort | Qingyu Xia |
| collection | DOAJ |
| description | The conventional blind source separation independent component analysis method has the problem of low-separation performance. In addition, the basic butterfly optimization algorithm has the problem of insufficient search capability. In order to solve the above problems, an independent component analysis method based on the double-mutant butterfly optimization algorithm (DMBOA) is proposed in this paper. The proposed method employs the kurtosis of the signal as the objective function. By optimizing the objective function, blind source separation of the signals is realized. Based on the original butterfly optimization algorithm, DMBOA introduces dynamic transformation probability and population reconstruction mechanisms to coordinate global and local search, and when the optimization stagnates, the population is reconstructed to increase diversity and avoid falling into local optimization. The differential evolution operator is introduced to mutate at the global position update, and the sine cosine operator is introduced to mutate at the local position update, hence, enhancing the local search capability of the algorithm. To begin, 12 classical benchmark test problems were selected to evaluate the effectiveness of DMBOA. The results reveal that DMBOA outperformed the other benchmark algorithms. Following that, DMBOA was utilized for the blind source separation of mixed image and speech signals. The simulation results show that the DMBOA can realize the blind source separation of an observed signal successfully and achieve higher separation performance than the compared algorithms. |
| first_indexed | 2024-03-10T00:53:54Z |
| format | Article |
| id | doaj.art-7da17926625a460086fc5b01f1ce16a0 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T00:53:54Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7da17926625a460086fc5b01f1ce16a02023-11-23T14:46:43ZengMDPI AGSensors1424-82202022-05-012211397910.3390/s22113979Blind Source Separation Based on Double-Mutant Butterfly Optimization AlgorithmQingyu Xia0Yuanming Ding1Ran Zhang2Minti Liu3Huiting Zhang4Xiaoqi Dong5Communication and Network Laboratory, Dalian University, Dalian 116622, ChinaCommunication and Network Laboratory, Dalian University, Dalian 116622, ChinaCommunication and Network Laboratory, Dalian University, Dalian 116622, ChinaNational Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaCommunication and Network Laboratory, Dalian University, Dalian 116622, ChinaCommunication and Network Laboratory, Dalian University, Dalian 116622, ChinaThe conventional blind source separation independent component analysis method has the problem of low-separation performance. In addition, the basic butterfly optimization algorithm has the problem of insufficient search capability. In order to solve the above problems, an independent component analysis method based on the double-mutant butterfly optimization algorithm (DMBOA) is proposed in this paper. The proposed method employs the kurtosis of the signal as the objective function. By optimizing the objective function, blind source separation of the signals is realized. Based on the original butterfly optimization algorithm, DMBOA introduces dynamic transformation probability and population reconstruction mechanisms to coordinate global and local search, and when the optimization stagnates, the population is reconstructed to increase diversity and avoid falling into local optimization. The differential evolution operator is introduced to mutate at the global position update, and the sine cosine operator is introduced to mutate at the local position update, hence, enhancing the local search capability of the algorithm. To begin, 12 classical benchmark test problems were selected to evaluate the effectiveness of DMBOA. The results reveal that DMBOA outperformed the other benchmark algorithms. Following that, DMBOA was utilized for the blind source separation of mixed image and speech signals. The simulation results show that the DMBOA can realize the blind source separation of an observed signal successfully and achieve higher separation performance than the compared algorithms.https://www.mdpi.com/1424-8220/22/11/3979blind source separationindependent component analysisbutterfly optimization algorithmdynamic transformation probabilitypopulation reconstruction mechanismdifferential evolution operator |
| spellingShingle | Qingyu Xia Yuanming Ding Ran Zhang Minti Liu Huiting Zhang Xiaoqi Dong Blind Source Separation Based on Double-Mutant Butterfly Optimization Algorithm Sensors blind source separation independent component analysis butterfly optimization algorithm dynamic transformation probability population reconstruction mechanism differential evolution operator |
| title | Blind Source Separation Based on Double-Mutant Butterfly Optimization Algorithm |
| title_full | Blind Source Separation Based on Double-Mutant Butterfly Optimization Algorithm |
| title_fullStr | Blind Source Separation Based on Double-Mutant Butterfly Optimization Algorithm |
| title_full_unstemmed | Blind Source Separation Based on Double-Mutant Butterfly Optimization Algorithm |
| title_short | Blind Source Separation Based on Double-Mutant Butterfly Optimization Algorithm |
| title_sort | blind source separation based on double mutant butterfly optimization algorithm |
| topic | blind source separation independent component analysis butterfly optimization algorithm dynamic transformation probability population reconstruction mechanism differential evolution operator |
| url | https://www.mdpi.com/1424-8220/22/11/3979 |
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