Title :
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
fDate :
6/1/2008 12:00:00 AM
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;
Journal_Title :
Magnetics, IEEE Transactions on
DOI :
10.1109/TMAG.2007.915316