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Mathematical Problems in Engineering
Volume 2016, Article ID 5481602, 8 pages
http://dx.doi.org/10.1155/2016/5481602
Research Article

Glowworm Swarm Optimization and Its Application to Blind Signal Separation

1College of Computer Science, Communication University of China, Beijing 100024, China
2Business College, Beijing Union University, Beijing 100025, China

Received 31 December 2015; Revised 27 March 2016; Accepted 7 April 2016

Academic Editor: Marco Mussetta

Copyright © 2016 Zhucheng Li and Xianglin Huang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.