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...

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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
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Abstract	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.
AbstractList	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.
Author	Yang, Chun Xia Zhang, Jian Tong, Mei Song
Author_xml	– sequence: 1 givenname: Chun Xia orcidid: 0000-0003-2445-8692 surname: Yang fullname: Yang, Chun Xia email: chunxiay@shnu.edu.cn organization: Shanghai Engineering Research Center of Intelligent Education and Bigdata, Shanghai Normal University, Shanghai, China – sequence: 2 givenname: Jian orcidid: 0000-0003-0660-2240 surname: Zhang fullname: Zhang, Jian organization: Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, China – sequence: 3 givenname: Mei Song orcidid: 0000-0001-6317-1008 surname: Tong fullname: Tong, Mei Song email: mstong@tongji.edu.cn organization: Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, China
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SubjectTerms	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
Title	A Hybrid Quantum-Behaved Particle Swarm Optimization Algorithm for Solving Inverse Scattering Problems
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