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 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.

Automated summary

This article introduces a hybrid inversion approach based on the QPSO method to solve electromagnetic inverse problems, aiming to evaluate the effectiveness in reconstructing 2-D dielectric scatterers and expand the contribution of excellent particles by introducing a weighted mean best position. The hybrid approach, HQPSO, combines the advantages of linear reconstruction algorithms and stochastic optimization algorithms to ensure accuracy and improve computational efficiency in large-scale optimization problems.