Evolutionary Multitasking Sparse Reconstruction: Framework and Case Study

Real-world applications typically have multiple sparse reconstruction tasks to be optimized. In order to exploit the similar sparsity pattern between different tasks, this paper establishes an evolutionary multitasking framework to simultaneously optimize multiple sparse reconstruction tasks using a...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation 2019-10, Vol.23 (5), p.733-747
Main Authors: Li, Hao, Ong, Yew-Soon, Gong, Maoguo, Wang, Zhenkun
Format: Article
Language:English
Subjects:
Evolutionary algorithm
Evolutionary algorithms
Hyperspectral imaging
hyperspectral unmixing
Image reconstruction
Mathematical analysis
Matrix algebra
Matrix methods
Multitasking
multitasking optimization
Optimization
Signal reconstruction
Sociology
sparse reconstruction
Statistics
Task analysis
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Summary:Real-world applications typically have multiple sparse reconstruction tasks to be optimized. In order to exploit the similar sparsity pattern between different tasks, this paper establishes an evolutionary multitasking framework to simultaneously optimize multiple sparse reconstruction tasks using a single population. In the proposed method, the evolutionary algorithm aims to search the locations of nonzero components or rows instead of searching sparse vector or matrix directly. Then the within-task and between-task genetic transfer operators are employed to reinforce the exchange of genetic material belonging to the same or different tasks. The proposed method can solve multiple measurement vector problems efficiently because the length of decision vector is independent of the number of measurement vectors. Finally, a case study on hyperspectral image unmixing is investigated in an evolutionary multitasking setting. It is natural to consider a sparse unmixing problem in a homogeneous region as a task. Experiments on signal reconstruction and hyperspectral image unmixing demonstrate the effectiveness of the proposed multitasking framework for sparse reconstruction.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2018.2881955