NSGA-Ⅱ算法的改进及其在多段翼型缝道参数优化中的应用

An Improvement to NSGA-Ⅱalgorithm and its application in optimization design of multi-element airfoil

提出了NSGA-Ⅱ算法的一个改进方法,对加入新种群的父代种群个体进行精英筛选,从而增加了新种群中新个体的数目,以实现更好的全局寻优。选取一个有理论解的优化问题,对算法全局寻优能力的改进进行了验证。随后使用改进后的NSGA-Ⅱ算法作为核心算法搭建了集群并行优化平台,对多段翼型的缝道参数进行优化设计,获得了较为满意的结果。优化验证算例显示,该集群并行优化平台具有较高的效率和可行性。

This paper proposes that an elite selection is applied to the parent population before it is directly added into the combined population,as is done in the original NSGA-Ⅱ method.This modification can better avoid the algorithm′s premature convergence to local optimums,and better globally explore the sample space.An analytical test problem with many local optimums is solved using the improved method.The result is compared with those of several other optimization methods.The improvements of the proposed modification are confirmed.Then the improved NSGA-Ⅱ method is used to find the optimum element positions and deflection angles of the landing configuration of a multi-element airfoil,to maximize the lift coefficient.The optimal result is close to that from the original NSGA-Ⅱ,while the optimum is achieved with fewer iterations.This satisfactory result shows that the improved optimization method is feasible and efficient for practical use.