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

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 optimizati...

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Vydané v:	The Artificial intelligence review Ročník 55; číslo 4; s. 3153 - 3181
Hlavní autori:	Erkoc, Murat Emre, Karaboga, Nurhan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Dordrecht Springer Netherlands 01.04.2022 Springer Springer Nature B.V
Predmet:	Algorithms Artificial Intelligence Comparative analysis Comparative studies Computational geometry Computer Science Convex analysis Convexity Intelligence Knee Mathematical optimization Multiple objective analysis Objectives Optimization Optimization algorithms Optimization techniques Recovery Search methods Signal reconstruction Theory Multi-objective optimization Knee region Sparse reconstruction Evolutionary algorithm Local search method Compressed sensing
ISSN:	0269-2821, 1573-7462
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Abstract	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.
AbstractList	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.
Audience	Academic
Author	Erkoc, Murat Emre Karaboga, Nurhan
Author_xml	– sequence: 1 givenname: Murat Emre orcidid: 0000-0002-2388-5329 surname: Erkoc fullname: Erkoc, Murat Emre email: merkoc@erciyes.edu.tr organization: Electrical and Electronics Engineering, Erciyes University – sequence: 2 givenname: Nurhan surname: Karaboga fullname: Karaboga, Nurhan organization: Electrical and Electronics Engineering, Erciyes University
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Keywords	Multi-objective optimization Knee region Sparse reconstruction Evolutionary algorithm Local search method Compressed sensing
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publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2010.04.014
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SubjectTerms	Algorithms Artificial Intelligence Comparative analysis Comparative studies Computational geometry Computer Science Convex analysis Convexity Intelligence Knee Mathematical optimization Multiple objective analysis Objectives Optimization Optimization algorithms Optimization techniques Recovery Search methods Signal reconstruction Theory
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Title	A comparative study of multi-objective optimization algorithms for sparse signal reconstruction
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