Reconstruction of One-Dimensional Dielectric Scatterers Using Differential Evolution and Particle Swarm Optimization

A comparison between differential evolution (DE) and particle swarm optimization (PSO) in solving 1-D small-scale inverse scattering problems is presented. In this comparison, the efficiency of both aforementioned optimization techniques is examined for permittivity and conductivity profile reconstruction problems. The comparison is carried out under the same conditions of initial population of candidate solutions and number of iterations. Numerical results indicate that both optimization methods are reliable tools for inverse scattering applications even when noisy measurements are considered. In the particular case of small-scale problems investigated in this letter, DE outperforms the PSO in terms of reconstruction accuracy. This is considered an indicative result and not generally applicable.

[1]  Anyong Qing,et al.  Electromagnetic inverse scattering of multiple two-dimensional perfectly conducting objects by the differential evolution strategy , 2003 .

[2]  Anyong Qing,et al.  Dynamic differential evolution strategy and applications in electromagnetic inverse scattering problems , 2006, IEEE Trans. Geosci. Remote. Sens..

[3]  Constantinos S. Hilas,et al.  An inverse scattering approach based on the differential E-formulation , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[4]  P. M. van den Berg,et al.  Total variation as a multiplicative constraint for solving inverse problems. , 2001, IEEE transactions on image processing : a publication of the IEEE Signal Processing Society.

[5]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[6]  Matteo Pastorino,et al.  Electromagnetic detection of dielectric scatterers using phaseless synthetic and real data and the memetic algorithm , 2003, IEEE Trans. Geosci. Remote. Sens..

[7]  A.S. Mohan,et al.  A Microparticle Swarm Optimizer for the Reconstruction of Microwave Images , 2007, IEEE Transactions on Antennas and Propagation.

[8]  Andrea Massa,et al.  Full-vectorial three-dimensional microwave imaging through the iterative multiscaling strategy-a preliminary assessment , 2005, IEEE Geoscience and Remote Sensing Letters.

[9]  A. Massa,et al.  Computational approach based on a particle swarm optimizer for microwave imaging of two-dimensional dielectric scatterers , 2005, IEEE Transactions on Microwave Theory and Techniques.

[10]  A. N. Tikhonov,et al.  Solutions of ill-posed problems , 1977 .

[11]  M. Pastorino.  Stochastic Optimization Methods Applied to Microwave Imaging: A Review , 2007, IEEE Transactions on Antennas and Propagation.

[12]  Andrea Massa,et al.  An integrated multiscaling strategy based on a particle swarm algorithm for inverse scattering problems , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[13]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[14]  Ioannis T. Rekanos,et al.  Shape Reconstruction of a Perfectly Conducting Scatterer Using Differential Evolution and Particle Swarm Optimization , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Krzysztof A. Michalski,et al.  Electromagnetic imaging of circular–cylindrical conductors and tunnels using a differential evolution algorithm , 2000 .

[16]  P. M. Berg,et al.  Iterative forward and inverse algorithms based on domain integral equations for three-dimensional electric and magnetic objects , 2004 .

[17]  M. Kamyab,et al.  TRUNCATED COSINE FOURIER SERIES EXPANSION METHOD FOR SOLVING 2-D INVERSE SCATTERING PROBLEMS , 2008 .

[18]  Andrea Massa,et al.  Reconstruction of two-dimensional buried objects by a differential evolution method , 2004 .