Multiobjective Optimization Based on Response Surface Model and Its Application to Engineering Shape Design

Author

Xie, Dexin ; Sun, Xiaowen ; Bai, Baodong ; Yang, Shiyou

Author_Institution

Sch. of Electr. Eng., Shenyang Univ. of Technol., Shenyang

Volume

44

Issue

6

fYear

2008

fDate

6/1/2008 12:00:00 AM

Firstpage

1006

Lastpage

1009

Abstract

A combined method is presented to deal with the practical engineering problems of multiobjective optimization. The nondominated sorting genetic algorithm II (NSGA-II) is adopted as a searching tool for the Pareto-optimal solutions, which is improved by using a new crossover operator. The response surface model (RSM) based on the radial basis function is used to reduce the computational effort. The application of the method to the shape optimization process of a permanent magnet assembly for magnetic resonance imaging devices is described, and the numerical results show that the method is feasible and efficient for the engineering shape optimization.

Keywords

Pareto optimisation; genetic algorithms; magnetic resonance imaging; permanent magnets; radial basis function networks; crossover operator; engineering shape design; magnetic resonance imaging; multiobjective optimization; nondominated sorting genetic algorithm II Pareto-optimal solutions; permanent magnet; radial basis function; response surface model; shape optimization; Genetic algorithm; main magnet of MRI; multiobjective optimization; shape optimization;

fLanguage

English

Journal_Title

Magnetics, IEEE Transactions on

Publisher

ieee

ISSN

0018-9464

Type

jour

DOI

10.1109/TMAG.2007.915316

Filename

4527026