Aiming at the disadvantages of the whale optimization algorithm(WOA), such as easy to fall into local optimal solution and slow convergence speed, an improved whale optimization algorithm is proposed. Firstly, Circle chaotic sequence is used to replace the initial population randomly generated in the original algorithm to improve the diversity of the initial individuals. Then, a one-dimensional small hole imaging reverse learning strategy is proposed to increase the diversity of the optimal location and improve the ability of the algorithm to get rid of the local optimum. Finally, the adaptive weights of fusion Beta distribution and inverse incomplete $\varGamma$ function are proposed which can coordinate the search ability of the algorithm under the premise of retaining the advantages of the whale optimization algorithm. The performance of the improved whale optimization algorithm is evaluated using Wilcoxon test, MAE and other methods through simulation experiments on 10 benchmark functions. The experimental results show that the improved algorithm has a great improvement in solving efficiency and stability, and the optimization accuracy and convergence speed of the algorithm are also better than the original algorithm.