eprintid: 513
rev_number: 5
eprint_status: archive
userid: 5
dir: disk0/00/00/05/13
datestamp: 2011-07-08
lastmod: 2013-06-30 08:56:16
status_changed: 2013-06-30 08:56:16
type: techreport
metadata_visibility: show
item_issues_count: 0
creators_name: Rocca, Paolo
creators_name: Massa, Andrea
title: Evolutionary-based Optimization Techniques for Inverse Scattering: A Review
ispublished: pub
subjects: TU
full_text_status: public
abstract: The use of stochastic global optimizers has had a non?negligible impact on several areas of research and industry and they have been effectively applied to several problems in engineering and sciences [1]. Thanks to the availability and growing of computational resources with the large diffusion of modern computers, optimization techniques based on Evolutionary Algorithms (EAs) have received a wide attention because of their attractive features. As a matter of fact, EAs are hill?climbing algorithms and do not require the differentiation of the cost function, which is a "must" for gradientbased methods. They are based on stochastic iterative procedures where a pool of trial solutions is used to sample the solution space at each iteration thus improving the search capability as compared to single?agent techniques (e.g., Simulated Annealing). A?priori information can also be easily introduced in terms of additional constraints on the actual solution or the boundaries of the solution space. Moreover, they can directly deal with real values as well as with coded representations of the unknowns (e.g., binary coding). Their main drawback (i.e., the convergence rate) has been also further contrasted by exploiting their implicit and explicit parallelism thanks to modern computer clusters [2].
date: 2011-01
date_type: published
institution: University of Trento
department: informaticat
refereed: FALSE
referencetext: [1] R. L. Haupt and D. H. Werner, Genetic Algorithms in Electromagnetics. Hoboken, Ney Jersey: John Wiley & Sons, 2007. [2] A. Massa et al., "Parallel GA?based approach for microwave imaging applications," IEEE Trans. Antennas Propag., vol. 53, pp. 3118-3127, Oct. 2005. [3] M. Pastorino, "Stochastic optimization methods applied to microwave imaging: A review," IEEE Trans. Antennas Propag., vol. 55, no. 3, pp. 538-548, Mar. 2007. [4] P. Rocca et al., "Evolutionary optimization as applied to inverse scattering problems," Inverse Problems, vol. 25, p. 1-41, 2009. [5] S. Caorsi, A. Massa, and M. Pastorino, "A computational technique based on a real-coded genetic algorithm for microwave imaging purposes," IEEE Trans. Geosci. Remote Sens., vol. 38, pp. 1697-1708, 2000. [6] A. Qing, "Electromagnetic inverse scattering of multiple two-dimensional perfectly conducting objects by the differential evolution strategy," IEEE Trans. Antennas Propag., vol. 51, pp. 1251-1262, 2003. [7] A. Massa, M. Pastorino, and A. Randazzo, "Reconstruction of two-dimensional buried objects by a differential evolution method," Inverse Problems, vol. 20, pp. 135-150, 2004. [8] M. Donelli and A. Massa, "Computational approach based on a particle swarm optimizer for microwave imaging of two?dimensional dielectric scatterers," IEEE Trans. Microwave Theory Tech., vol. 53, pp. 1761-1776, 2005. [9] S. Caorsi et al., "Detection of buried inhomogeneous elliptic cylinders by a memetic algorithm," IEEE Trans. Antennas Propag., vol. 51, pp. 2878-2884, 2003.
citation:   Rocca, Paolo and Massa, Andrea  (2011) Evolutionary-based Optimization Techniques for Inverse Scattering: A Review.  [Technical Report]     
document_url: http://www.eledia.org/students-reports/513/1/DISI-11-177.C190.pdf