A whale optimization algorithm based on the chaotic search strategy (CWOA) is proposed to overcome the drawbacks of being difficult to coordinate the exploration and exploitation ability, and easily trapped into local optimum. In the proposed algorithm, the chaotic opposition-based learning strategy is used to generate initial population, which strengthens the diversity of population in the global searching process. Then, a nonlinearly chaotic disturbance cooperative updating strategy for the convergence factor and inertia weight is designed to balance the exploration and exploitation ability. Finally, the chaotic search strategy for optimum individual is combined with evolutionary population updating to avoid the possibility of being trapped into local optimum. The optimization experiments are conducted on the 10 benchmark functions and 6 composite functions. Simulation results show that the proposed CWOA has fast convergence and more precise convergence than other comparison algorithms.