Particle swarm optimization method for parameter selecting in Tikhonov regularization method for solving inverse problems

• We also provide the Tikhonov method's essential analysis for tackling inverse problems. • We go over how to choose the Tikhonov regularization parameter by implementing the particle swarm optimization (PSO) technique. • The effectiveness of combining these two approaches particle swarm optimization and Tikhonov is demonstrated to be highly practicable. In this article the Tikhonov method used as a regularization technique for solving the inverse problem liner operator equation of the first kind with noisy and noise-free data. We also provide the Tikhonov method's essential analysis for tackling inverse problems. Following that, we go over how to choose the Tikhonov regularization parameter by implementing the Particle Swarm Optimization (PSO) technique. The effectiveness of combining these two approaches particle swarm optimization and Tikhonov is demonstrated to be highly practicable.

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Publication Details

Journal
e-Prime - Advances in Electrical Engineering Electronics and Energy
Published
2024-09-06
DOI
https://doi.org/10.1016/j.prime.2024.100750
Citations
4
Primary Topic
Numerical methods in inverse problems
Type
article
Field-Weighted Citation Impact
3.56
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article

Particle swarm optimization method for parameter selecting in Tikhonov regularization method for solving inverse problems

Alhumaima Ali Subhi, H. K. Al-Mahdawi
4 citations
e-Prime - Advances in Electrical Engineering Electronics and Energy
Numerical methods in inverse problems
3.56
article

Particle swarm optimization method for parameter selecting in Tikhonov regularization method for solving inverse problems

Alhumaima Ali Subhi, H. K. Al-Mahdawi
article en
4 citations

Abstract

• We also provide the Tikhonov method's essential analysis for tackling inverse problems. • We go over how to choose the Tikhonov regularization parameter by implementing the particle swarm optimization (PSO) technique. • The effectiveness of combining these two approaches particle swarm optimization and Tikhonov is demonstrated to be highly practicable. In this article the Tikhonov method used as a regularization technique for solving the inverse problem liner operator equation of the first kind with noisy and noise-free data. We also provide the Tikhonov method's essential analysis for tackling inverse problems. Following that, we go over how to choose the Tikhonov regularization parameter by implementing the Particle Swarm Optimization (PSO) technique. The effectiveness of combining these two approaches particle swarm optimization and Tikhonov is demonstrated to be highly practicable.

e-Prime - Advances in Electrical Engineering Electronics and EnergyVol. 10
University of Diyala (IQ)
Openalex Percentile: Top 6%
Numerical methods in inverse problems
3.56
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