An Intelligent Grey Wolf Optimizer Algorithm for Distributed Compressed Sensing

Distributed Compressed Sensing (DCS) is an important research area of compressed sensing (CS). This paper aims at solving the Distributed Compressed Sensing (DCS) problem based on mixed support model. In solving this problem, the previous proposed greedy pursuit algorithms easily fall into suboptima...

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Bibliographic Details
Published in:Computational intelligence and neuroscience 2018-01, Vol.2018 (2018), p.1-10
Main Authors: Xu, Yonggang, Xu, Jingwen, Hua, Gang, Liu, Haiqiang
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Computer applications
Computer simulation
Detection
Greedy algorithms
Mathematical optimization
Neural networks
Optimization algorithms
Software
Wolves
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Summary:Distributed Compressed Sensing (DCS) is an important research area of compressed sensing (CS). This paper aims at solving the Distributed Compressed Sensing (DCS) problem based on mixed support model. In solving this problem, the previous proposed greedy pursuit algorithms easily fall into suboptimal solutions. In this paper, an intelligent grey wolf optimizer (GWO) algorithm called DCS-GWO is proposed by combining GWO and q-thresholding algorithm. In DCS-GWO, the grey wolves’ positions are initialized by using the q-thresholding algorithm and updated by using the idea of GWO. Inheriting the global search ability of GWO, DCS-GWO is efficient in finding global optimum solution. The simulation results illustrate that DCS-GWO has better recovery performance than previous greedy pursuit algorithms at the expense of computational complexity.
ISSN:1687-5265
1687-5273
DOI:10.1155/2018/1723191