Hybrid optimization in electromagnetics using sensitivity information from a neuro-fuzzy model

Author

Rashid, Kashif ; Ramirez, Jaime A. ; Freeman, Ernest M.

Author_Institution

Dept. of Electr. & Electron. Eng., Imperial Coll. of Sci., Technol. & Med., London, UK

Volume

36

Issue

4

fYear

2000

fDate

7/1/2000 12:00:00 AM

Firstpage

1061

Lastpage

1065

Abstract

The use of sensitivity information from a neuro-fuzzy model for the purpose of optimization is investigated in this paper. This approach permits the application of classic deterministic or hybrid optimization methods in establishing the global minimum of any approximated objective function using neuro-fuzzy modeling. For nondifferentiable functions this approach is of great benefit. An analytical problem and the TEAM 22 benchmark problem are investigated. Results using the genetic algorithm method and the sequential quadratic programming method in sequence show the usefulness of the formulation

Keywords

electrical engineering computing; electromagnetic field theory; fuzzy neural nets; genetic algorithms; optimisation; quadratic programming; sensitivity; TEAM 22 benchmark problem; analytical problem; approximated objective function; deterministic method; electromagnetics; genetic algorithm method; global minimum; hybrid optimization; neuro-fuzzy model; nondifferentiable functions; search process; sensitivity information; sequential quadratic programming method; Cost function; Design optimization; Electromagnetic modeling; Genetic algorithms; Optimization methods; Predictive models; Quadratic programming; Sampling methods; Signal generators; Stochastic processes;

fLanguage

English

Journal_Title

Magnetics, IEEE Transactions on

Publisher

ieee

ISSN

0018-9464

Type

jour

DOI

10.1109/20.877624

Filename

877624