To locate the damage of the structure efficiently and judge the damage degree, this paper proposes an improved fruit fly optimization algorithm (IFOA). Aiming at the problem of poor convergence of the standard fruit fly optimization algorithm in the face of complex structure damage identification, the IFOA introduces the concept of collaborative search of two subpopulations. The IFOA divides the entire population into positive subgroups and negative subgroups based on the individual taste concentration results. Among them, the positive subgroup uses the improved dynamic adaptive search step size to perform a fine search locally to improve its local search ability. Negative subgroups continue to use the standard fruit fly optimization algorithm for optimization, taking advantage of the powerful global search capabilities of the standard fruit fly optimization algorithm. It enables the algorithm to balance global and local search capabilities, prevents the algorithm from falling into local optimum, and speeds up the convergence speed and accuracy of the algorithm. Simulation results show that IFOA can effectively identify the damage location and damage degree of the structure, and it still performs well when facing the complex steel truss damage identification.

为了有效地定位结构的损伤并判断损伤程度，提出了一种改进的果蝇优化算法（IFOA）。针对面对复杂结构破坏识别的标准果蝇优化算法收敛性差的问题，IFOA引入了两个子种群协同搜索的概念。IFOA根据个人的口味集中结果将整个人群分为阳性亚组和阴性亚组。其中，正子组使用改进的动态自适应搜索步长在本地进行精细搜索以提高其本地搜索能力。负子组继续使用标准果蝇优化算法进行优化，利用标准果蝇优化算法强大的全局搜索功能。它使算法能够平衡全局和局部搜索能力，防止算法陷入局部最优状态，并加快算法的收敛速度和准确性。仿真结果表明，IFOA可以有效地识别结构的损伤位置和损伤程度，并且在面对复杂的钢桁架损伤识别时仍然表现良好。