针对黑翅鸢优化算法（black-winged kite optimization algorithm, BKA）在全局探索与局部开发能力之间存在的不平衡，以及易陷入局部最优解的问题，提出了一种改进的黑翅鸢优化算法（improved BKA, IBKA）。首先，采用Tent混沌映射策略对种群进行初始化，提高种群的多样性。其次，在BKA的捕食行为中引入了一种动态透镜成像学习策略，以提高算法摆脱局部最优解的概率。最后，在BKA的迁移过程中集成了夫琅禾费衍射搜索策略，旨在提升算法的性能，实现快速寻优。实验结果表明，所提出的改进方法能够有效增强算法性能，经过改进后的IBKA具有更高的搜索精度、更快的收敛速度，并且展现出较强的实用性。

In order to solve the imbalance between global exploration and local development capabilities of the black-winged kite optimization algorithm (BKA), and the problem that it is easy to fall into the local optimal solution, an improved BKA (IBKA) was proposed. Firstly, the Tent chaotic mapping strategy was used to initialize the population and improve the diversity of the population. Secondly, a dynamic lens imaging learning strategy was introduced into the predatory behavior of BKA to improve the probability of the algorithm getting rid of the local optimal solution. Finally, in the migration process of BKA, the diffraction search strategy of Fraunhofer was integrated to improve the performance of the algorithm and achieve fast optimization. Experimental results show that the proposed improved method can effectively enhance the performance of the algorithm, and the IBKA has higher search accuracy, faster convergence speed, and strong practicability.