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.