Multilayer dielectric filter design using a multiobjective evolutionary algorithm

• Published in: IEEE transactions on antennas and propagation Vol. 53; no. 11; pp. 3625 - 3632
• Main Authors: Venkatarayalu, N.V., Ray, T., Gan, Y.-B.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-11-2005; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Applied sciences; Band pass filters; Computational modeling; Design optimization; Detection, estimation, filtering, equalization, prediction; Dielectric materials; Dielectrics; Evolutionary algorithms; Evolutionary computation; Exact sciences and technology; Fitness; Frequency response; Genetic algorithms; Information, signal and communications theory; Low pass filters; Mathematical analysis; Mathematical models; Multilayers; Multiobjective optimization; Nonhomogeneous media; Objectives; optimization of dielectric filters; Searching; Signal and communications theory; Signal, noise; stochastic algorithm; Studies; Telecommunications and information theory; Testing; Performance evaluation; Optimal filter; Dielectric materials; Evolutionary algorithm; Multiobjective programming; Flexibility; Modeling; Optimization; Algorithm performance; stochastic algorithm; Genetic algorithm; Multiobjective optimization; optimization of dielectric filters; Low pass filter; Frequency response; Open market
• ISSN: 0018-926X; 1558-2221
• DOI: 10.1109/TAP.2005.858565

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 nondominated sorting genetic algorithm (NSGA-II) for the low-pass filter design where our results are better.