Glowworm Swarm Optimization and Its Application to Blind Signal Separation

Traditional optimization algorithms for blind signal separation (BSS) are mainly based on the gradient, which requires the objective function to be continuous and differentiable, so the applications of these algorithms are very limited. Moreover, these algorithms have problems with the convergence s...

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Bibliographic Details
Published inMathematical problems in engineering Vol. 2016; no. 2016; pp. 1 - 8
Main Authors Li, Zhucheng, Huang, Xianglin
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Publishing Corporation 01.01.2016
Hindawi Limited
Subjects
Accuracy
Algorithms
Convergence
Functions (mathematics)
Genetic algorithms
Kurtosis
Mathematical analysis
Matlab
Optimization
Optimization algorithms
Particle swarm optimization
Signal processing
Swarm intelligence
Beijing China
China
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Summary:Traditional optimization algorithms for blind signal separation (BSS) are mainly based on the gradient, which requires the objective function to be continuous and differentiable, so the applications of these algorithms are very limited. Moreover, these algorithms have problems with the convergence speed and accuracy. To overcome these drawbacks, this paper presents a modified glowworm swarm optimization (MGSO) algorithm based on a novel step adjustment rule and then applies MGSO to BSS. Taking kurtosis of the mixed signals as the objective function of BSS, MGSO-BSS succeeds in separating the mixed signals in Matlab environment. The simulation results prove that MGSO is more effective in capturing the global optimum of the objective function of the BSS algorithm and has faster convergence speed and higher accuracy, compared with particle swarm optimization (PSO) and GSO.
Bibliography:ObjectType-Article-1
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ISSN:1024-123X
1563-5147
DOI:10.1155/2016/5481602