Sparse Reconstruction with Bat Algorithm and Orthogonal Matching Pursuit

被引：0

作者：

Zhang, Chunmei ^{[1
]}

Cai, Xingjuan ^{[1
]}

Shi, Zhentao ^{[1
]}

机构：

[1] Taiyuan Univ Sci & Technol, Complex Syst & Computat Intelligence Lab, Taiyuan 030024, Shanxi, Peoples R China

来源：

INTELLIGENT COMPUTING THEORIES AND APPLICATION, ICIC 2017, PT I | 2017年 / 10361卷

关键词：

Sparse reconstruction; Bat algorithm; Orthogonal matching pursuit; ARTIFICIAL BEE COLONY; SIGNAL RECOVERY; OPTIMIZATION ALGORITHM; L(1) MINIMIZATION; FIREFLY ALGORITHM; SEARCH; ATTRACTION; SYSTEMS;

D O I：

10.1007/978-3-319-63309-1_5

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Sparse reconstruction problem is a typical research topic in compressed sensing theory and applications, and many algorithms are designed to solve it. As one of the most well-known reconstruction algorithm, orthogonal matching pursuit (OMP) is widely used in many applications. However, due to the limited global search capability, it is often fallen into the local optimal. In this paper, bat algorithm, a new population-based swarm intelligent algorithm, is incorporated into the methodology of OMP to increase the global search capability. To test the performance, two different signals are employed to compare, and simulation results show our algorithm increases the performance significantly.

引用

页码：48 / 56

页数：9

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