Electromagnetic scattering of metallic cylinders of arbitrary shape by using asynchronous particle swarm optimization and non-uniform steady state genetic algorithm

[[abstract]]Two techniques for the shape reconstruction of multiple metallic cylinders from scattered fields are investigated in this paper, in which two-dimensional configurations are involved. After an integral formulation, the method of moment (MoM) is applied to solve it numerically. Two separate perfect-conducting cylinders of unknown shapes are buried in one half-space and illuminated by the transverse magnetic (TM) plane wave from the other half space. Based on the boundary condition and the measured scattered field, a set of nonlinear integral equation is derived and the imaging problem is reformulated into optimization problem. The non-uniform steady state genetic algorithm (NU-SSGA) and asynchronous particle swarm optimization (APSO) are employed to find out the global extreme solution of the object function. Numerical results demonstrate even when the initial guesses are far away from the exact shapes, and the multiple scattered fields between two conductors are serious, good reconstruction can be obtained. In addition, the effect of Gaussian noise on the reconstruction results is investigated and the numerical simulation shows that the reconstruction results are good and acceptable, as long as the SNR is greater than 20 dB.[[incitationindex]]SCI[[booktype]]電子版[[booktype]]紙

Nondestructive Evaluation of Buried Dielectric Cylinders by Asynchronous Particle Swarm Optimization

[[abstract]]This paper presents the study of time domain inverse scattering for a two-dimensional inhomogeneous dielectric cylinder buried in a slab medium via the finite difference time domain (FDTD) method and the asynchronous particle swarm optimization (APSO) method. For the forward scattering part, the FDTD method was employed to calculate the scattered E fields. Base on the scattering fields, these inverse scattering problems were transformed into optimization problems. The APSO method was applied to reconstruct the permittivity of the two-dimensional inhomogeneous dielectric cylinder. In addition, the effects of Gaussian noise on the reconstruction results were investigated. Numerical results show that even when the measured scattered fields were contaminated with Gaussian noise, APSO was able to yield good reconstructed quality.[[notice]]補正完畢[[incitationindex]]SCI[[incitationindex]]EI[[booktype]]電子

Inverse Scattering of Dielectric Cylindrical Target Using Dynamic Differential Evolution and Self-Adaptive Dynamic Differential Evolution

[[abstract]]The inverse problem under consideration is to reconstruct the characteristic of scatterer from the scattering E field. Dynamic differential evolution (DDE) and self-adaptive dynamic differential evolution (SADDE) are stochastic-type optimization approach that aims to minimize a cost function between measurements and computer-simulated data. These algorithms are capable of retrieving the location, shape, and permittivity of the dielectric cylinder in a slab medium made of lossless materials. The finite-difference time-domain (FDTD) is employed for the analysis of the forward scattering. The comparison is carried out under the same conditions of initial population of candidate solutions and number of iterations. Numerical results indicate that SADDE outperforms the DDE a little in terms of reconstruction accuracy.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子

Comparative Study of Some Population-based Optimization Algorithms on Inverse Scattering of a Two-Dimensional Perfectly Conducting Cylinder in Slab Medium

[[abstract]]The application of four techniques for the shape reconstruction of a 2-D metallic cylinder buried in dielectric slab medium by measured the cattered fields outside is studied in the paper. The finite-difference time-domain (FDTD) technique is employed for electromagnetic analyses for both the forward and inverse scattering problems, while the shape reconstruction problem is transformed into optimization one during the course of inverse scattering. Then, four techniques including asynchronous particle swarm optimization (APSO), PSO, dynamic differential evolution (DDE) and self-adaptive DDE (SADDE) are applied to reconstruct the location and shape of the 2-Dmetallic cylinder for comparative purposes. The statistical performances of these algorithms are compared. The results show that SADDE outperforms PSO, APSO and DDE in terms of the ability of exploring the optima. However, these results are considered to be indicative and do not generally apply to all optimization problems in electromagnetics.[[incitationindex]]SCI[[incitationindex]]EI[[booktype]]紙本[[booktype]]電子

Electromagnetic imaging of Buried Perfectly Conducting Cylinders TArgets Using APSO

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Bioinspired metaheuristic algorithms for global optimization

This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions

Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF