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 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.

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Chaos Glowworm Swarm Optimization Algorithm Based on Cloud Model for Face Recognition

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001420560091 ◽

2020 ◽

Vol 34 (12) ◽

pp. 2056009

Author(s):

Guangyu Zhou ◽

Aijia Ouyang ◽

Yuming Xu

Keyword(s):

Face Recognition ◽

Cloud Model ◽

Cuckoo Search ◽

Global Optimum ◽

Convergence Speed ◽

Invasive Weed ◽

Swarm Optimization ◽

Hybrid Particle Swarm Optimization ◽

Glowworm Swarm Optimization ◽

Better Than

To overcome the shortcomings of the basic glowworm swarm optimization (GSO) algorithm, such as low accuracy, slow convergence speed and easy to fall into local minima, chaos algorithm and cloud model algorithm are introduced to optimize the evolution mechanism of GSO, and a chaos GSO algorithm based on cloud model (CMCGSO) is proposed in the paper. The simulation results of benchmark function of global optimization show that the CMCGSO algorithm performs better than the cuckoo search (CS), invasive weed optimization (IWO), hybrid particle swarm optimization (HPSO), and chaos glowworm swarm optimization (CGSO) algorithm, and CMCGSO has the advantages of high accuracy, fast convergence speed and strong robustness to find the global optimum. Finally, the CMCGSO algorithm is used to solve the problem of face recognition, and the results are better than the methods from literatures.

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Blind Source Separation based on Whale Optimization Algorithm

MATEC Web of Conferences ◽

10.1051/matecconf/201817303052 ◽

2018 ◽

Vol 173 ◽

pp. 03052

Author(s):

CHU Ding-li ◽

CHEN Hong ◽

CHEN Han-yi

Keyword(s):

Particle Swarm Optimization ◽

Blind Source Separation ◽

Optimization Algorithm ◽

Source Separation ◽

Whale Optimization Algorithm ◽

Signal Separation ◽

Blind Signal Separation ◽

Swarm Optimization ◽

Blind Signal ◽

Whale Optimization

Aiming at the problem of linear instantaneous aliasing in blind source separation, a new method of blind signal separation using whale optimization algorithm is proposed in this paper, which provides a new research idea and method for blind signal separation. The new method adopts the method of independent component analysis, optimizes the objective function by using the whale optimization algorithm, realizes the blind separation of instantaneous aliasing signals, and effectively avoids the problem of complex parameters and slow convergence rate of the particle swarm optimization algorithm. The simulation results show that the performance of whale optimization algorithm is better than that of particle swarm optimization for blind source separation, and it is effective for blind signal separation.

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