基于改进蝴蝶优化算法的结构损伤识别

Structural Damage Identification Based on Improved Butterfly Optimization Algorithm

针对传统的蝴蝶优化算法(butterfly optimization algorithm,简称BOA)全局搜索能力差、收敛速度慢、结构频率对损伤不敏感等问题,提出基于改进蝴蝶优化算法(improved butterfly optimization algorithm,简称IBOA)与小波包能量曲率的结构损伤识别方法。首先,在传统蝴蝶优化算法基础上引入聚类竞争学习机制和混沌精英学习机制,得到改进蝴蝶优化算法,此算法可以更好地实现局部搜索和全局搜索间的平衡,收敛速度更快、计算精度更高;其次,利用小波包能量曲率建立目标函数进一步提高识别结果精度;最后,分别以简支梁数值算例和8自由度弹簧-质量块实验验证了该方法的有效性。研究结果表明,即使考虑环境噪声和模型误差等不利因素,所提出的方法仍可以有效识别结构的损伤位置和程度。

Since the traditional butterfly optimization algorithm may(BOA) have the problems of poor global search ability and slow convergence speed, while structural frequency is insensitive to the damage, a structural damage identification method based on improved butterfly optimization algorithm(IBOA) and wavelet packet energy curvature is proposed in the present study. The improved butterfly optimization algorithm is initially proposed by introducing clustering competitive learning mechanism and chaotic elite learning mechanism into the traditional butterfly optimization algorithm. With faster convergence speed and higher calculation accuracy, the proposed improved algorithm is able to achieve a good balance between local search and global search. In addition, the wavelet packet energy curvature is evaluated in the objective function to further improve the accuracy of identification results. The effectiveness of the proposed method is verified by numerical examples of simply-supported beam and 8-DOF springmass experiment. The results show that the proposed method can accurately identify the damage location and extent of the structure, even if the environmental noise and model error are considered.