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Journals / 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
Pages
263–271
Publication Date
2012-02-01
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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 Key Takeaways

🎯
Objective & Hypothesis:This paper introduces a modified particle swarm algorithm to handle multiobjective optimization problems.
📊
Key Findings & Data:In multiobjective PSO algorithms, the determination of Pareto optimal solutions depends directly on the strategy of assigning a best local guide to each particle.
💡
Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:FET modeling · multiobjective optimization · pareto optimal analysis

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.

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.

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Cite This Article

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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References (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.
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