A comparative study of multi-objective optimization algorithms for sparse signal reconstruction
Author:
Funder
Erciyes Üniversitesi
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Linguistics and Language,Language and Linguistics
Link
https://link.springer.com/content/pdf/10.1007/s10462-021-10073-5.pdf
Reference64 articles.
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2. Arjoune Y, Kaabouch N, El Ghazi H, Tamtaoui A (2017) Compressive sensing: performance comparison of sparse recovery algorithms. Paper presented at the 2017 IEEE 7th annual computing and communication workshop and conference Ieee Ccwc-2017
3. Bandyopadhyay S, Saha S, Maulik U, Deb K (2008) A simulated annealing-based multiobjective optimization algorithm: AMOSA. IEEE Trans Evol Comput 12:269–283. https://doi.org/10.1109/Tevc.2007.900837
4. Baraniuk RG (2007) Compressive sensing [Lecture Notes]. IEEE Signal Process Mag 24:118-121. https://doi.org/10.1109/MSP.2007.4286571
5. Blumensath T, Davies ME (2008) Iterative thresholding for sparse approximations. J Fourier Anal Appl 14:629–654. https://doi.org/10.1007/s00041-008-9035-z
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