Fabrication of a band-reject filter using dielectric-supported air-gapped microstriplines

A multiobjective evolutionary technique is applied to design dielectric filters useful in microwave communications technology. The optimal geometry of the filters is derived by utilizing two different multiobjective optimization algorithms. The first one is the Nondominated Sorting Genetic Algorithm‐II (NSGA‐II), which is a popular multiobjective genetic algorithm. The second algorithm is based on multiobjective Particle Swarm Optimization with fitness sharing (MOPSO‐fs). MOPSO‐fs algorithm is a novel Pareto PSO algorithm that produces the Pareto front in a fast and efficient way. In the present work, MOPSO‐fs is compared with NSGA‐II to optimize the geometry of the filters under specific requirements concerning the frequency response of the filters. Several examples are studied to exhibit the efficiency of the multiobjective evolutionary optimizers and also the ability of the technique to derive optimal structures that can be used in practice. © 2007 Wiley Periodicals, Inc. Microwave Opt Technol Lett 49: 2324–2329, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.22755

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Dielectric filter optimal design suitable for microwave communications by using multiobjective evolutionary algorithms

Goudos

Zaharis

Salazar-Lechuga

et al. 2007

Micro & Optical Tech Letters

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Fabrication of a band-reject filter using dielectric-supported air-gapped microstriplines

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Dielectric filter optimal design suitable for microwave communications by using multiobjective evolutionary algorithms

Dielectric filter optimal design suitable for microwave communications by using multiobjective evolutionary algorithms

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