A Hybrid Quantum-Behaved Particle Swarm Optimization Algorithm for Solving Inverse Scattering Problems

A hybrid inversion approach based on the quantum-behaved particle swarm optimization (QPSO) method is presented in this article to solve electromagnetic inverse problems. Inverse scattering problems are ill-posed and are often transformed into optimization problems by defining a suitable cost functi...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on antennas and propagation Jg. 69; H. 9; S. 5861 - 5869
Hauptverfasser: Yang, Chun Xia, Zhang, Jian, Tong, Mei Song
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.09.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Computational efficiency
Computing costs
Cost function
Electromagnetic diffraction
Electromagnetic scattering
Electromagnetics
Evolutionary algorithms
Genetic algorithms
Imaging
Inverse problems
Inverse scattering
microwave imaging
Optimization
Particle swarm optimization
quantum-behaved particle swarm optimization (PSO)
Statistics
ISSN:0018-926X, 1558-2221
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:A hybrid inversion approach based on the quantum-behaved particle swarm optimization (QPSO) method is presented in this article to solve electromagnetic inverse problems. Inverse scattering problems are ill-posed and are often transformed into optimization problems by defining a suitable cost function, which can be minimized by evolutionary algorithms. This article is aimed at assessing the effectiveness of a customized QPSO in reconstructing 2-D dielectric scatterers. The bottleneck that restricts the application of the evolutionary algorithm in large-scale optimization problems is its computational cost. In this article, the diffraction tomographic image is used as an initial guess for the QPSO. Moreover, a weighted mean best position according to the fitness values of the particles is introduced to expand the contribution of excellent particles on population evolution. This hybrid approach, denoted as HQPSO, makes full use of the complementary advantages of linear reconstruction algorithms and stochastic optimization algorithms and is, thus, able to ensure accuracy and improve computational efficiency. Numerical experiments for different types of dielectric objects are performed with synthetic and experimental inverse-scattering data.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-926X
1558-2221
DOI:10.1109/TAP.2021.3060592