Nonlinear Blind Source Separation Algorithm Using Glowworm Swarm Optimization with Baffle Effect

Pubo Huang; Yu Wei

doi:10.11959/j.issn.1000-0801.2015213

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Nonlinear Blind Source Separation Algorithm Using Glowworm Swarm Optimization with Baffle Effect

Research and development | 更新时间：2024-06-05

- Nonlinear Blind Source Separation Algorithm Using Glowworm Swarm Optimization with Baffle Effect
- Telecommunications Science Vol. 31, Issue 9, Pages: 97-102(2015)
- 作者机构：
  
  1. 武汉科技大学武汉430070
  2. 华中科技大学武汉430047
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2015213
  CLC：
- Published Online：2015-09，
  
  Published：20 September 2015
- 稿件说明：
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Pubo Huang, Yu Wei. Nonlinear Blind Source Separation Algorithm Using Glowworm Swarm Optimization with Baffle Effect[J]. Telecommunications science, 2015, 31(9): 97-102.
DOI：

Pubo Huang, Yu Wei. Nonlinear Blind Source Separation Algorithm Using Glowworm Swarm Optimization with Baffle Effect[J]. Telecommunications science, 2015, 31(9): 97-102. DOI： 10.11959/j.issn.1000-0801.2015213.

摘要

摘要：根据萤火虫算法的特点，针对萤火虫算法较早进入局部收敛的不足进行改进，引入了“挡板效应”的方法，扩大种群多样性，并提出了BGSO算法。在分析了非线性盲源分离模型的基础上，利用系统两次遍历萤火虫群，比较评价函数，获得最优解的方式。其有效性被仿真结果所证实。

Abstract

According to the characteristics of glowworm swarm optimization（GSO）algorithm

to overcome the disadvantages of premature convergence

a new method called “baffle effect”was proposed. The diversity of swarm was expanded. Based on the analysis of the model of nonlinear blind source separation（NBSS）

using traversing glowworm swarm twice

comparing evaluation function

the best result was get. Their validity is confirmed by effect of the signal separation test.

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references

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