抄録
In the process of optimization with a large nonlinear dynamic analysis, a large amount of computational cost is required to obtain the optimal design. A large portion of this cost can be avoided using Response Surface Model (RSM) by approximating the more costly analysis. Therefore, the optimization with RSM is studied by various approaches. Sequential Approximate Optimization (SAO) that allows RSM to be updated with new design points during optimization obtains final design better then the method of non updating RSM. But, the design is not always optimal design. In this paper, we propose a method that cross SAO’s design and a good design of current process of optimization by the real type crossover model(RXM). RXM was referred crossover models of Real coded Genetic Algorithms, was developed. We applied a multimodal parameter problem, a cantilever problem and a nonlinear dynamic analysis problem, obtained good results.
