A novel sparse reconstruction method based on multi-objective Artificial Bee Colony algorithm

Erkoç M. E., Karaboğa N.

SIGNAL PROCESSING, cilt.189, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 189
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1016/j.sigpro.2021.108283
  • Dergi Adı: SIGNAL PROCESSING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Communication Abstracts, Computer & Applied Sciences, INSPEC, zbMATH
  • Anahtar Kelimeler: Compressed sensing, Multi-objective optimization, Sparse reconstruction, Artificial Bee colony algorithm, EVOLUTIONARY ALGORITHMS, THRESHOLDING ALGORITHM, SIGNAL RECONSTRUCTION, OPTIMIZATION, DECOMPOSITION, SHRINKAGE, RECOVERY
  • Erciyes Üniversitesi Adresli: Evet

Özet

Compressed sensing is a signal processing method that performs the compressing and sensing processes at the same time. Sparse signal reconstruction is one of the most important issues of compressed sensing. The developments in sparse signal reconstruction methods directly affect the performance of the com-pressed sensing process. Many sparse signal reconstruction methods have been proposed in the literature. In general, these algorithms are classified as convex optimization, non-convex optimization, and greedy algorithms. In addition, multi-objective optimization algorithms have started to be used in sparse sig-nal reconstruction lately. A sparse signal reconstruction method based on a Multi-objective Artificial Bee Colony algorithm is proposed in this study. The proposed algorithm optimizes the sparsity and measure-ment error at the same time. Furthermore, it uses the iterative half thresholding algorithm to improve the convergence acceleration of the method. The proposed method was evaluated by using various test signals. Additionally, it was compared with other sparse signal reconstruction algorithms. According to the obtained results, the proposed method has some superiority over the compared algorithms. (c) 2021 Elsevier B.V. All rights reserved.