Abstract: The white shark optimization algorithm (WSO) is a new meta-heuristic algorithm inspired by the hunting behavior of white sharks. The WSO is prone to enter a premature state when solving high-dimensional optimization problem, and the accuracy of the optimization results is low. Therefore, an improved white shark optimizer algorithm (IWSO) is proposed. Firstly, IWSO initializes the population using Sinusoidal chaotic mapping to improve the population diversity and the distribution of the initial solution in the solution space. Secondly, bird flock search behavior is introduced to endow the shark swimming velocity with adaptive dynamic inertia weights to improve the algorithm's convergence speed. Eventually, the elite shark cosine mutation strategy is introduced in the algorithm position update phase. The periodic characteristic of the cosine function is utilized to drive the shark individuals to finely exploit in the finite neighborhood of the elite shark and improve the convergence accuracy. Performance comparison experiments are conducted on 23 well-known benchmark functions and CEC2014 functions, and the results indicate that IWSO outperforms the six comparison algorithms and is suitable for solving function optimization problems.