Shape Reconstruction of a Perfectly Conducting Scatterer Using Differential Evolution and Particle Swarm Optimization

The shape reconstruction of a perfectly conducting 2-D scatterer by inverting transverse magnetic scattered field measurements is investigated. The reconstruction is based on evolutionary algorithms that minimize the discrepancy between measured and estimated scattered field data. A closed cubic B-spline expansion is adopted to represent the scatterer contour. Two algorithms have been examined the differential-evolution (DE) algorithm and the particle swarm optimization (PSO). Numerical results indicate that the DE algorithm outperforms the PSO in terms of reconstruction accuracy and convergence speed. Both techniques have been tested in the case of simulated measurements contaminated by additive white Gaussian noise.