基于非支配排序的并行加点方法研究及应用

The surrogate-based optimization method can greatly improve the efficiency of high-precision numerical optimization, while the infill sampling method is very important for the optimization result and efficiency. Several training samples can be infilled using the parallel infill sampling approach in a single step, fully using computer resources and increasing efficiency. In this article, based on the surrogate-based optimization framework including sub-optimization, three multi-objective parallel infill methods are constructed, using the prediction value, prediction variance and expected improvement(EI) function value as sub-optimization objectives. Besides, a strategy for selecting samples based on non-dominated sorting is proposed. Next, take the six-hump camel back(SC) function, the 2-dimensional griewank(GN) function, the 5-dimensional Rosenbrock function and the 10-dimensional high-dimension 1(HD1) function as unconstrained optimization examples, and the 7-dimensional G9 function as the constrained optimization example, the three multi-objective parallel infill sampling methods are compared with the hybrid parallel infill sampling methods. The outcomes demonstrate the superiority of the multi-objective parallel infill technique. Finally, the lift-drag ratio optimization of the two-dimensional multi-foil at take-off state was carried out by using the multi-objective infill sampling method，the hybrid parallel infill method and the genetic algorithm based on computational fluid dynamics(CFD). The optimization findings demonstrate the usefulness of the parallel infill sampling method in engineering issues by increasing the lift-to-drag ratio by 14% after a minimal amount of CFD evaluation under the constraint that the lift coefficient does not drop.