A comparison of two generazed response surface methods for optimisation in electromagnetics

COMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering

Volumn 20, Issue 3, 2001, Pages 740-752

  Rashid, K ^a   Farina, M ^b   Ramirez, J A ^c   Sykulski, J K ^a   Freeman, E M ^d  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

^b UNIVERSITY OF PAVIA   (Italy)

^c FEDERAL UNIVERSITY OF MINAS GERAIS   (Brazil)

^d UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

Author keywords

Design; Electromagnetics; Evolution strategies; Fuzzy systems; Neural networks

Indexed keywords

FINITE ELEMENT METHOD; FUNCTIONS; FUZZY SETS; MAGNETOELECTRIC EFFECTS; NEURAL NETWORKS; OPTIMIZATION;

RESPONSE SURFACE METHOD;

PROBLEM SOLVING;

EID: 0035788012     PISSN: 03321649     EISSN: None     Source Type: Journal    
DOI: 10.1108/03321640110393743     Document Type: Article

References (14)

1. Alotto, P., Caiti, A., Molinari, G. and Repetto, M. (1996), 'A multiquadrics based algorithm for the acceleration of simulated annealing optimisation procedures, IEEE Trans. Magn., Vol. 32 No. 3, pp. 1198-201.
2. Chiu, S. (1994), A cluster estimation method with extension to fuzzy model identification, IEEE Trais. Fuzzy Systems, Vol. 2 No. 3, September, pp. 1240-5.
3. Farina, M. and Sykulski, J.K. (2000), 'Comparative study of evolution strategies combined with approximation techniques for practical electromagnetic optimisation problems, in press ~ in IEEE Tratis. Mag., 2001.
4. Fowlkes, W.Y. and Creveling, CM. (1995), Engineering Metlwds for Robust Product Design, Addison-Wesley, New York, NY.
5. Gallardo, J.A. and Lowther, D.A. (1999), The optimisation of electromagnetic devices using niching genetic algorithms, COMPEL, Vol. 18 No. 3, pp. 285-97.
6. Malik, Z. and Rashid, K. (2000), A comparison of optimisation by response surface methodology with neuro-fuzzy models, IEEE Trans. Magn., Vol. 36 No. 1, January, pp. 241-57.
7. Mohammed, O.A and Uler, F.G. (1997), A hybrid technique for the optimal design of electromagnetic devices using direct search and genetic algorithms, IEEE Trans. Magii, Vol. 33 No. 2, pp.19314.
8. Pahner, U. and Hameyer, K. (1999), Adaptive coupling of differential evolution and multiquadrics approximation for the tuning of the optimization process, Proceedings of COMPUMAG Sapporo, 25-28 October, pp. 116-17.
9. Rashid, K., Ramirez, J. and Freeman, EM. (2000a), Hybrid optimisation in electromagnetics using sensitivity information from a neuro-fuzzy model, IEEE Trans. Magn., Vol. 36 No. 3.
10. Rashid, K., Ramirez, J.A. and Freeman, EM. (2000b), A general approach for extracting sensitivity analysis from a neuro-fuzzy model, IEEE Trans. Magn., Vol. 36 No. 3.
11. Rashid, K., Ramirez, J.A. and Freeman, EM. (2000c), Optimisation of electromagnetics devices using sensitivity information from clustered neuro-fuzzy models, in press IEEE Trans., May 2001.
12. Stom, R and Price, K. (n.d.), Differential evolution: a simple and efficient adaptive scheme for global optimization over continuous spaces, TR95-012, ICSI, http.icsi.berkeley.edu/ storn/litera.html.
13. Sykulski, J.K., Al-Khoury, A.H. and Goddard, K.F. (2000), Minimal function calls approach with on-line learning and dynamic weighting for computationally intensive design optimisation, in press IEEE Trans. Mag, 2001.
14. Uler, G.F. and Mohammed, O.A. (1996), Ancillary techniques for the practical implementation of GAs to the optimal design of electromagnetic devices, IEEE Trans. Mag., Vol. 32 No. 3.