Due to the strong local exploitation ability and the weak global exploration ability of the location update formula, the grasshopper optimization algorithm(GOA) is easy to fall into local optimum and easy to prematurely converge. Therefore, this paper proposes a hybrid Cauchy mutation and uniform distribution of the grasshopper optimization algorithm(HCUGOA). Firstly, inspired by the Cauchy operator and particle swarm optimization algorithm, a location update method with segmentation idea is proposed to increase the diversity of the population and to enhance the global exploration ability. Then, the fusion of Cauchy mutation and opposition-based learning and the variation of the optimal position which is the target value improve the ability of the algorithm to jump out of the local optimum. Finally, in order to better balance the global exploration and local exploitation, the uniform distribution function is introduced into the nonlinear control parameter c, so that a new random adjustment strategy can be built. The optimization performance of the improved algorithm is evaluated by a sets of simulation experiments and Wilcoxon's test on 12 benchmark functions and modern CEC 2014 functions. The experimental results show that the HCUGOA has been greatly improved in terms of convergence accuracy and convergence speed.