Comparison of steady-state genetic algorithm and asynchronous particle swarm optimization on inverse scattering of a partially immersed metallic cylinder

The inverse problem under consideration is to reconstruct the characteristic of scatterer from the scattering E field. Steady-state genetic algorithm (SSGA) and asynchronous particle swarm optimization (APSO) are stochastic-type optimization approach that aims to minimize a cost function between measurements and computer-simulated data. Thus, the shape of metallic cylinder can be obtained by minimizing the objective function. After an integral formulation, a discretization using the method of moment (MoM) is applied. Numerical results indicate that the asynchronous particle swarm optimization (APSO) outperforms steady-state genetic algorithm (SSGA) in terms of reconstruction accuracy and convergence speed.