A comparative study of multi-objective optimization algorithms for sparse signal reconstruction


Artificial Intelligence Review, vol.55, no.4, pp.3153-3181, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 55 Issue: 4
  • Publication Date: 2022
  • Doi Number: 10.1007/s10462-021-10073-5
  • Journal Name: Artificial Intelligence Review
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, Educational research abstracts (ERA), Index Islamicus, INSPEC, Library and Information Science Abstracts, Library, Information Science & Technology Abstracts (LISTA), Metadex, Psycinfo, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.3153-3181
  • Keywords: Multi-objective optimization, Compressed sensing, Sparse reconstruction, Evolutionary algorithm, Knee region, Local search method, EVOLUTIONARY ALGORITHMS, RECOVERY, DECOMPOSITION
  • Kayseri University Affiliated: No


© 2021, The Author(s), under exclusive licence to Springer Nature B.V.The development of the efficient sparse signal recovery algorithm is one of the important problems of the compressive sensing theory. There exist many types of sparse signal recovery methods in compressive sensing theory. These algorithms are classified into several categories like convex optimization, non-convex optimization, and greedy methods. Lately, intelligent optimization techniques like multi-objective approaches have been used in compressed sensing. Firstly, in this paper, the basic principles of the compressive sensing theory are summarized. And then, brief information about multi-objective algorithms, local search methods, and knee point selection methods are given. Afterward, multi-objective sparse recovery methods in the literature are reviewed and investigated in accordance with their multi-objective optimization algorithm, the local search method, and the knee point selection method. Also in this study, examples of multi-objective sparse reconstruction methods are designed according to the existing studies. Finally, the designed algorithms are tested and compared by using various types of sparse reconstruction test problems.