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中文摘要：
针对粒子群等随机优化算法计算量大的缺点，发展了基于Kriging模型的优化方法。采用改进的量子粒子群算法对Kriging模型的相关模型参数进行优化，以提高代理模型预测精度，并与具有双层结构的粒子群算法相结合。采用雷诺平均N—S方程流场求解器与多目标非线性适应值加权方法，对高维度多目标多约束的跨声速机翼进行了优化，设计的机翼具有理想的压力分布，降低了机翼阻力系数，并且有效控制了低头力矩和翼根弯矩，表明该方法具有较强的工程实用性。
英文摘要：
A optimization method based on Kriging surrogate model is developed in order to reduce the number of evaluations of global optimum algorithms such as PSO. A double-layer particle swarm optimization arithmetic based on the Kriging model which enhanced precision by using a modified QPSO to compute the global optimums of correlation model parameters. Reynolds-Averaged Navier-Stokes flow solver and nonlinear weighted sum of multi-objective method is used to optimize transonic wing with multi-objective and multi-restriction. The results demonstrated that the proposed approach achieves significant drag and moment reduction with ideal pressure distributions over the wing and can be used in an engineering environment.
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