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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。
In order to overcome the shortcomings of stochastic optimization such as particle swarm optimization, a Kriging-based optimization method is developed. The improved Quantum Particle Swarm Optimization algorithm is used to optimize the related model parameters of Kriging model to improve the prediction accuracy of the proxy model and to combine with the two-layer particle swarm optimization algorithm. The Reynolds-averaged Navier-Stokes equations and the multi-objective non-linear fitness-weighted method were used to optimize the transonic high-dimensional multi-objective and multi-constrained transonic wing with ideal pressure distribution and reduced wing resistance Coefficient, and effectively control the bow moment and the root bending moment, indicating that the method has strong engineering practicability.