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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Bibliographic Details
Published in:The Artificial intelligence review Vol. 55; no. 4; pp. 3153 - 3181
Main Authors: Erkoc, Murat Emre, Karaboga, Nurhan
Format: Journal Article
Language:English
Published: Dordrecht Springer Netherlands 01.04.2022
Springer
Springer Nature B.V
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:0269-2821
1573-7462
DOI:10.1007/s10462-021-10073-5