SAMPLING STRATEGY USING GENETIC ALGORITHM (GA) FOR OPTIMIZING ENGINEERING DESIGN

摘要

PPROBLEM TO BE SOLVED: To disclose a sampling strategy using a genetic algorithm (GA) for optimizing engineering design. PSOLUTION: Design of a product is optimized by using one set of design variables, target, and constraints. Then a proper number of samples for design of experiments method (DOE) are defined in such a way that respective points show a specific combination of the design variables that do not overlap other. A sample selection strategy is based on the genetic algorithm (GA), and a computer-aided engineering analysis (CAE) (such as finite element analysis method, finite difference analysis, mesh-free analysis) is carried out for respective samples when selecting the samples based on the GA. A meta model is created to approximate a result of the CAE analysis for all the DOE samples. When the meta model meets (for example accuracy is within a tolerance), an optimized "best" design can be found by using the meta model as a function evaluator to an optimization method. Finally the CAE analysis is carried out for verifying the optimized "best" design. PCOPYRIGHT: (C)2010,JPO&INPIT