Reducing the Computational Cost of Inverse Scattering Problems With Evolutionary Algorithms

In an inverse scattering problem, the constituent parameters of a scatterer are identified from the scattered field. In this paper, we use the real-coded clonal selection algorithm (RCSA) along with an iterative absorbing boundary condition (IABC) to control the computational cost of the analysis during the optimization process. A relatively less accurate boundary condition is used in the global search and a progressively improved boundary condition for the local search. Some results are presented to illustrate the accuracy and the significant reduction in the computation time obtained with our approach.