文献类型：期刊文献

英文题名：Glowworm Swarm Optimization and Its Application to Blind Signal Separation

作者：Li, Zhucheng[1,2];Huang, Xianglin[1]

第一作者：李著成;Li, Zhucheng

通讯作者：Li, ZC[1];Li, ZC[2]

机构：[1]Commun Univ China, Coll Comp Sci, Beijing 100024, Peoples R China;[2]Beijing Union Univ, Coll Business, Beijing 100025, Peoples R China

第一机构：Commun Univ China, Coll Comp Sci, Beijing 100024, Peoples R China

通讯机构：[1]corresponding author), Commun Univ China, Coll Comp Sci, Beijing 100024, Peoples R China;[2]corresponding author), Beijing Union Univ, Coll Business, Beijing 100025, Peoples R China.|[1141721]北京联合大学商务学院;[11417]北京联合大学;

年份：2016

卷号：2016

外文期刊名：MATHEMATICAL PROBLEMS IN ENGINEERING

收录：;EI(收录号：20162302474585);Scopus(收录号：2-s2.0-84971671290);WOS:【SCI-EXPANDED(收录号:WOS:000375673700001)】；

语种：英文

外文关键词：MATLAB - Particle swarm optimization (PSO)

摘要：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.