Abstract:Due to the strong local exploitation ability and the weak global exploration ability of the location update formula, the grasshopper optimization algorithm (GOA) was easy to fall into local optimum and easy to prematurely converge. Therefore, this paper proposed a hybrid Cauchy mutation and uniform distribution of grasshopper optimization algorithm (HCUGOA). Firstly, inspired by the cauchy operator and particle swarm optimization algorithm, a location update method with segmentation idea was proposed to increase the diversity of the population and to enhance the global exploration ability. Secondly, the fusion of cauchy mutation and opposition-based learning, the variation of the optimal position, which was, the target value, improved 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 was introduced into the nonlinear control parameter c, so that could build a new random adjustment strategy. 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 algorithm has been greatly improved in terms of convergence accuracy and convergence speed.