Parameter identification for an aerofoil with an external store system based on an enhanced Jaya algorithm is proposed. The discrepancy between the measured and calculated displacement responses is used to formulate the objective function, which will be optimized by the enhanced Jaya algorithm. In the enhanced Jaya algorithm, two variants are introduced: the k-means clustering is employed to utilize the colony information fully; and a new updating equation that focuses on the exploration search is designed for the best-so-far solution in each cycle. During the identification process, both the inherent and extrinsic uncertainties existing in the aerofoil system are considered. The final identification results show that owing to its strong global search ability and robustness, the proposed enhanced Jaya can clearly identify the aerofoil system, even when the system is under quasi-periodic oscillations. The identification accuracy is better than that acquired by other approaches.

提出了一种基于增强Jaya算法的外存储翼型参数识别。测量和计算的位移响应之间的差异用于制定目标函数，该目标函数将通过增强的 Jaya 算法进行优化。在增强的 Jaya 算法中，引入了两个变体：k- 均值聚类用于充分利用菌落信息；并且针对每个循环中的最佳解决方案设计了一个专注于探索搜索的新更新方程。在识别过程中，考虑了翼型系统中存在的内在和外在不确定性。最终识别结果表明，由于其强大的全局搜索能力和鲁棒性，所提出的增强型Jaya即使在系统处于准周期振荡的情况下也能清晰地识别出翼型系统。识别精度优于其他方法。