Multiobjective Optimization Using Coupled Response Surface Model and Evolutionary Algorithm.
Yongsheng Lian, Meng‐Sing Liou · AIAA Journal · 2005
In this work we develop an efficient approach for computationally expensive multiobjective design optimization problems. In this approach we bring together design of experiment, a response surface model, a genetic algorithm, and computational-fluid-dynamics analysis tools to provide an integrated optimization system. We use an improved hypercube sampling to preselect an array of design points on which the computational-fluid-dynamics code will run. Then a computationally cheap surrogate model is constructed based on response surface approximation. A real-coded genetic algorithm is then applied on the surrogate model to perform multiobjective optimization. Representative solutions are chosen from the Pareto-optimal front to verify against the computational-fluid-dynamics code. This proposed method is used in the redesign of a single-stage turbopump, a two-stage turbopump, and the NASA rotor67 transonic compressor blade