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Cross-Entropy Method for Combinatorial Mixed-Parameter Optimization of Waveguide Polarizers for Ku-Band | IEEE Conference Publication | IEEE Xplore

Cross-Entropy Method for Combinatorial Mixed-Parameter Optimization of Waveguide Polarizers for Ku-Band


Abstract:

In this work, a cross-entropy (CE) method-based combinatorial mixed parameter optimization approach is successfully implemented to design a wideband waveguide polarizer f...Show More

Abstract:

In this work, a cross-entropy (CE) method-based combinatorial mixed parameter optimization approach is successfully implemented to design a wideband waveguide polarizer for Ku-band applications. Technically, the choice of permittivity and corresponding width of dielectrics required to load the walls of the waveguide polarizer to produce an optimum design remains a severe challenge. We classify this as an optimization problem and address it using the CE method. The dielectric loading on the walls of a square waveguide polarizer is optimized to achieve a broader bandwidth and an axial ratio (AR) below 0.2 dB. The CE algorithm coded in MATLAB in conjunction with the full-wave simulations in CST MWS is implemented to increase the AR bandwidth and reduce the overall length of the polarizer.
Date of Conference: 27-29 December 2022
Date Added to IEEE Xplore: 24 January 2023
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ISSN Information:

Conference Location: Cairo, Egypt

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I. Introduction

With the increasing popularity of evolutionary optimization methods and the easy availability of unprecedented computational resources, solving complex non-linear Electromagnetic (EM) problems has become more accessible. Evolutionary Algorithms (EAs) can achieve an optimal solution with a high probability for complex high-dimensional EM problems, unlike the manual design approach, which is time-consuming and often produces sub-optimal solutions [1]. Due to strong mutual coupling and other propagation effects, optimizing antenna and EM components involves highly non-linear objective functions that exhibit an epistatic behavior [2]. Such complex antenna and EM component design require a full-wave simulation-based derivative-free optimization approach. Evolutionary Algorithms are a class of derivative-free optimization with superior exploration skills, and they can handle multi-objective, multi-dimensional, and multi-extremal optimization problems. EAs are conducive to parallel computing, which significantly reduces the optimization run time by distributing the task among multiple computers. In conjunction with full-wave EM solvers, EAs automate the antenna design process, which brings a sharp reduction in the cost of design and production [1], [3].

References

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