Permittivity distribution reconstruction of dielectric objects by a cascaded method

In this paper, we propose a method, which combines a genetic algorithm (GA) with a Newton-type iteration for the reconstruction of permittivity distribution of two-dimensional (2-D) dielectric objects. The method is based on a multi-illumination multiview processing. In particular, by taking account into the complete nonlinear formulations, the dielectric constant distribution of the objects could be highly-contrasted and complicated. First, the inverse problem is recast as a global nonlinear optimization problem, which is solved by a steady-state GA (SSGA). Then, the solution obtained by the SSGA is taken as an initial guess for the Newton-type iteration method. This method is tested by considering several numerical examples, and it is found that the performance of this combination method is better than the individual SSGA and the individual Newton-type iteration method. Numerical results show that satisfactory reconstruction has been obtained.