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

S. K. Goudos; Z. D. Zaharis; M. Salazar-Lechuga; P. I. Lazaridis; P. B. Gallion

doi:10.1002/mop.22755

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

S. K. Goudos, Z. D. Zaharis, M. Salazar-Lechuga, P. I. Lazaridis, P. B. Gallion

Research output: Contribution to journal › Article › peer-review

13 Citations (Scopus)

Abstract

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 tin 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.

Original language	English
Pages (from-to)	2324-2329
Number of pages	6
Journal	Microwave and Optical Technology Letters
Volume	49
Issue number	10
DOIs	https://doi.org/10.1002/mop.22755
Publication status	Published - Oct 2007
Externally published	Yes

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title = "Dielectric filter optimal design suitable for microwave communications by using multiobjective evolutionary algorithms",

abstract = "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 tin 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.",

keywords = "Dielectric filters, Evolutionary programming, Genetic algorithms, Microwave filters, Mutltiobjective optimization, Pareto optimization, Particle swarm optimization",

author = "Goudos, {S. K.} and Zaharis, {Z. D.} and M. Salazar-Lechuga and Lazaridis, {P. I.} and Gallion, {P. B.}",

year = "2007",

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AU - Goudos, S. K.

AU - Zaharis, Z. D.

AU - Salazar-Lechuga, M.

AU - Lazaridis, P. I.

AU - Gallion, P. B.

PY - 2007/10

Y1 - 2007/10

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AB - 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 tin 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.

KW - Dielectric filters

KW - Evolutionary programming

KW - Genetic algorithms

KW - Microwave filters

KW - Mutltiobjective optimization

KW - Pareto optimization

KW - Particle swarm optimization

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

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