Chimp optimization algorithms(ChOA) based on the convex lens imaging strategy is proposed to overcome the drawbacks of easily trapping into local optimum, slow convergence speed and low optimization precision. Firstly, the population is initialized by the Sobol sequence, which can increase the randomness and diversity of the population, and lay the foundation for the global optimization of the algorithm. Then, the opposition-based learning strategy based on convex lens imaging is introduced, which is applied it to the current optimal individual to generate new individuals, and improve the convergence accuracy and speed of the algorithm. At the same time, the water wave dynamic adaptive factor is added to the attacker's location update to enhance the ability of the algorithm to escape from the local optimum. Finally, The simulation experiments are conducted on the 10 benchmark functions, the Wilcoxon rank sum test and some parts of CEC2014 functions to evaluate the optimization performance of the improved algorithm. The experimental results show that the proposed algorithm has more significant improvement in optimization accuracy, convergence speed and robustness than the comparison algorithm. In addition, three mechanical optimization design experiments are conducted to test and analyze the feasibility and applicability of the improved algorithm.