Abstract: To overcome the problems of slow convergence, slow accuracy of the standard whale optimization algorithm, a nonlinear weight and a nonlinear convergence factor in whale optimization algorithm is proposed. Firstly, the improved Logistic chaotic mapping is applied to initial population. Then the linearly variable convergence factor is improved to a piecewise nonlinear convergence factor. At the same time, nonlinear inertia weights are added to enhance the exploration and exploitation capabilities of the whale optimization algorithm. Finally, seven benchmark functions are selected for testing. Experiments show that the improved algorithm has fast convergence and high precision.