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 paper 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 paper is aimed at assessing the effectiveness of a customized QPSO in reconstructing two-dimensional dielectric scatterers. The bottle-neck that restricts the application of evolutionary algorithm in large-scale optimization problems is its computational cost. In this paper, 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 the accuracy and improve the computational efficiency. Numerical experiments for different types of dielectric objects are performed with synthetic and experimental inverse-scattering data.