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Dergiler / Turkish Journal of Electrical Engineering and Computer Science / 2012 / Cilt: 20 - Sayı: 2

A modified particle swarm optimization algorithm and its application to the multiobjective FET modeling problem

Ufuk ÖZKAYA
Filiz GÜNEŞ
Sayfa
263–271
Yayın Tarihi
2012-02-01
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

FET models are also built for the 3 points sampled from the different locations of the Pareto front, and a discussion is presented for the Pareto relation between the scattering parameter performances and the operation bandwidth for each model.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:This paper introduces a modified particle swarm algorithm to handle multiobjective optimization problems.
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Temel Bulgular & Veri:In multiobjective PSO algorithms, the determination of Pareto optimal solutions depends directly on the strategy of assigning a best local guide to each particle.
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Sonuç & Katkı:FET models are also built for the 3 points sampled from the different locations of the Pareto front, and a discussion is presented for the Pareto relation between the scattering parameter performances and the operation bandwidth for each model.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:FET modeling · multiobjective optimization · pareto optimal analysis

Özet

This paper introduces a modified particle swarm algorithm to handle multiobjective optimization problems. In multiobjective PSO algorithms, the determination of Pareto optimal solutions depends directly on the strategy of assigning a best local guide to each particle. In this work, the PSO algorithm is modified to assign a best local guide to each particle by using minimum angular distance information. This algorithm is implemented to determine field-effect transistor (FET) model elements subject to the Pareto domination between the scattering parameters and operation bandwidth. Furthermore, the results are compared with those obtained by the nondominated sorting genetic algorithm-II. FET models are also built for the 3 points sampled from the different locations of the Pareto front, and a discussion is presented for the Pareto relation between the scattering parameter performances and the operation bandwidth for each model.

Abstract

This paper introduces a modified particle swarm algorithm to handle multiobjective optimization problems. In multiobjective PSO algorithms, the determination of Pareto optimal solutions depends directly on the strategy of assigning a best local guide to each particle. In this work, the PSO algorithm is modified to assign a best local guide to each particle by using minimum angular distance information. This algorithm is implemented to determine field-effect transistor (FET) model elements subject to the Pareto domination between the scattering parameters and operation bandwidth. Furthermore, the results are compared with those obtained by the nondominated sorting genetic algorithm-II. FET models are also built for the 3 points sampled from the different locations of the Pareto front, and a discussion is presented for the Pareto relation between the scattering parameter performances and the operation bandwidth for each model.

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Bu Makaleye Atıf Yap

ÖZKAYA, U., GÜNEŞ, F. (2012). A modified particle swarm optimization algorithm and its application to the multiobjective FET modeling problem. Turkish Journal of Electrical Engineering and Computer Science, Cilt: 20 - Sayı: 2, 263–271. https://ajindex.com/articles/189172

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Kaynakça (5)

  1. Table. FET model element values. FET model elements gm(S) Cgs(pF) Ri(Ω) Cds(pF) Rds(Ω) Cgd(pF) rg(Ω) rd(Ω) rS(Ω) 2044 1189 6168 1 Frequency (GHz) J. Kennedy, R.C. Eberhart, “Particle swarm optimization”, Proceedings of IEEE Conference on Neural Networks, Vol. 4, pp. 1942-1948, 1995.
  2. S. Mostaghim, J. Teich, “Strategies for Şnding good local guides in multi-objective particle swarm optimization (MOPSO)”, IEEE 2003 Swarm Intelligence Symposium, pp. 26-33, 2003.
  3. X. Hu, R. Eberhart, “Multiobjective optimization using dynamic neighborhood particle swarm optimization”, IEEE Proceedings of World Congress on Computational Intelligence, pp. 1677-1681, 2002. T. G¨unel, “A continuous hybrid approach to the FET modelling for the maximum transducer power gain”, Microwave and Optical Technology Letters, Vol. 35, pp. 348-351, 2002. C. Paoloni, “A simpliŞed procedure to calculate the power gain deŞnitions of FET’s”, IEEE Transactions on Microwave Theory and Technique, Vol. 48, pp. 470-474, 2000.
  4. K. Deb, A. Pratap, S. Agrawal, T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II”, IEEE Transactions on Evolutionary Computation, Vol. 6, pp.182-197, 2002.
  5. K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms, Chichester, UK, John Wiley & Sons, 2004.