Independent component analysis based on genetic algorithms

Wen, Gongjian; Zhang, Chunxiao; Lin, Zhaorong; Shang, Zhiming; Wang, Hongmin; Qian, Zhong‐Ji

doi:10.1109/icnc.2014.6975837

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…Luo et al [ 15 , 16 ] applied the improved fireworks algorithm (FA) to the radar signal processing, while the fireworks algorithm is prone to local extremum. Wen et al [ 17 ] used a genetic algorithm (GA) to ICA, although the local search capability of GA is limited.…”

Section: Introductionmentioning

confidence: 99%

Blind Source Separation Based on Double-Mutant Butterfly Optimization Algorithm

Xia

Ding

Zhang

et al. 2022

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

Blind Source Separation Based on Double-Mutant Butterfly Optimization Algorithm

Xia

Ding

Zhang

et al. 2022

Sensors

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Optimal Performance and Application for Firework Algorithm Using a Novel Chaotic Approach

Luo¹,

Jin²,

Li³

et al. 2020

IEEE Access

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Aimed at the bottleneck of Firework Algorithm's performance and the problem that it is easy to fall into the local extremum, this paper introduces a chaotic map approach and analyzes its statistical characteristics. New hybrid chaotic systems and a chaotic perturbation operator are designed. Based on different chaotic map approaches, a new Chaotic Firework Algorithm (CFWA) is proposed and simulated by typical test functions. The results show that performance of the Chaotic Firework Algorithm is generally better than the original algorithms, and the chaotic hybrid approaches are superior to single chaotic approaches. In order to further verify the application of the proposed algorithm in practical engineering problems, CFWA is applied to Blind Source Separation (BSS) of radar signals. As a result, it does well in sorting observed signals, and has faster convergence and better sorting performance than the traditional algorithms. In this way, the CFWA extends its engineering application.

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Independent component analysis based on genetic algorithms

Cited by 2 publications

References 21 publications

Blind Source Separation Based on Double-Mutant Butterfly Optimization Algorithm

Blind Source Separation Based on Double-Mutant Butterfly Optimization Algorithm

Optimal Performance and Application for Firework Algorithm Using a Novel Chaotic Approach

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