Please use this identifier to cite or link to this item: https://doi.org/10.1109/TAP.2005.858565
Title: Multilayer dielectric filter design using a multiobjective evolutionary algorithm
Authors: Venkatarayalu, N.V. 
Ray, T. 
Gan, Y.-B. 
Keywords: Multiobjective optimization
Optimization of dielectric filters
Stochastic algorithm
Issue Date: Nov-2005
Citation: Venkatarayalu, N.V., Ray, T., Gan, Y.-B. (2005-11). Multilayer dielectric filter design using a multiobjective evolutionary algorithm. IEEE Transactions on Antennas and Propagation 53 (11) : 3625-3632. ScholarBank@NUS Repository. https://doi.org/10.1109/TAP.2005.858565
Abstract: Design of multilayer dielectric filters involve the identification of suitable dielectric material and appropriate thicknesses of the layers that best satisfies the desired frequency response for the application. Such problems, like any other practical design optimization problem require simultaneous consideration of multiple objectives and constraints. In this paper, we introduce a multiobjective evolutionary algorithm that is capable of handling unconstrained and constrained, single and multiobjective problems without any restriction on the number and nature of variables, constraints and objectives. The algorithm handles constraints and objectives separately using two fitness measures derived out of nondominance, unlike most of its counterparts which use a single fitness measure. Unlike most evolutionary algorithms where only the good parents participate in mating, our algorithm ensures that all solutions participate in mating, which is useful for exploring highly nonlinear search spaces. The diversity of the solutions is controlled by the partner selection scheme that prefers elites with distant neighbors as mating partners. The results of two multiobjective test problems, three multilayer dielectric filter designs (low-pass, bandpass and stopband) and one variable layer low-pass filter design are presented in this paper to highlight the benefits offered by our algorithm in terms of modeling flexibility, computational efficiency and its ability to arrive at competitive nondominated designs. A comparison of our results with those obtained using a single objective aggregated formulation for the stopband filter design is also presented. We have also compared the performance of our algorithm with non-dominated sorting genetic algorithm (NSGA-II) for the low-pass filter design where our results are better. © 2005 IEEE.
Source Title: IEEE Transactions on Antennas and Propagation
URI: http://scholarbank.nus.edu.sg/handle/10635/111443
ISSN: 0018926X
DOI: 10.1109/TAP.2005.858565
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