Multi-Objective Optimization Based on Response Surface Model and its Application to Engineering Shape Design

摘要

A combined method is presented to deal withthe practical engineering problems of multi-objective optimization. The Non-dominated Sorting Genetic Algorithm II (NSGA II) is adopted as a searching tool for the optimization, which is improved by using a new crossover operator. The Response Surface Model (RSM) based on the Radial Basis Function (RBF) 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 (MRI) devices is described, and the numerical results show that the method is feasible and of high efficiency.