2009
DOI: 10.1088/0266-5611/25/12/123003
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Evolutionary optimization as applied to inverse scattering problems

Abstract: This review is aimed at presenting an overview of evolutionary algorithms (EAs) as applied to the solution of inverse scattering problems. The focus of this work is on the use of different population-based optimization algorithms for the reconstruction of unknown objects embedded in an inaccessible region when illuminated by a set of microwaves. Starting from a general description of the structure of EAs, the classical stochastic operators responsible for the evolution process are described. The extension to h… Show more

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Cited by 405 publications
(235 citation statements)
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“…Moreover, α CF , α SR , and α L are real positive weighting coefficients. In the P SO, the classical velocity and position update equations, respectively defined as [31] …”
Section: Iterative Optimization Approachmentioning
confidence: 99%
See 1 more Smart Citation
“…Moreover, α CF , α SR , and α L are real positive weighting coefficients. In the P SO, the classical velocity and position update equations, respectively defined as [31] …”
Section: Iterative Optimization Approachmentioning
confidence: 99%
“…The optimization is carried out by means the PSO [31], namely a stochastic global optimization algorithm able to effectively deal with non-convex (i.e., multi-minima) cost functions as well as real-valued unknowns as needed for the synthesis problems at hand.…”
Section: Introductionmentioning
confidence: 99%
“…Most inverse scattering methods assume the knowledge of incident fields illuminating an region of interest [1]- [3]. We have proposed an inverse scattering approach which does not require the information of the incident field [4].…”
Section: Introductionmentioning
confidence: 99%
“…In order to avoid nonuniqueness and instability as well as to prevent the retrieval of false solutions [28], several inversion strategies have been proposed based on (a) a suitable definition of the integral equations either in exact [29,30] or approximated [31][32][33][34][35] forms to model the scattering phenomena, (b) the exploitation of the available a-priori information on some features of the scenario/scatterers under test [15,[36][37][38][39] or/and the knowledge of input-output samples of data and reference solutions [40][41][42] and/or the information acquired during the inversion process [43][44][45][46][47], and (c) the use of suitable global optimization strategies [48][49][50][51][52][53][54][55]. Whatever the approach, inversion methods generally consider an optimization step aimed at minimizing/maximizing a suitably defined data-mismatch cost function through gradient or evolutionarybased algorithms with still not fully resolved drawbacks.…”
Section: Introductionmentioning
confidence: 99%
“…Whatever the approach, inversion methods generally consider an optimization step aimed at minimizing/maximizing a suitably defined data-mismatch cost function through gradient or evolutionarybased algorithms with still not fully resolved drawbacks. On the one hand, the use of local optimizers (e.g., gradient based) requires the optimization process starts in the "attraction basin" [54] of the global optimum to avoid being trapped into local minima (i.e., false solutions) of the cost function. On the other hand, global optimizers do not guarantee the retrieval of the global optimum within a finite amount of time/iterations.…”
Section: Introductionmentioning
confidence: 99%