Grasshopper optimization algorithm with nonlinear weight and cauchy mutation

To overcome the problems of slow convergence, falling into local optima of the standard grasshopper optimization algorithm, a nonlinear weight and cauchy mutation in grasshopperoptimization algorithm is proposed. Firstly, the good point set is applied to initial population and uniform population distribution.Then the linearlyweightis improved to a nonlinear weight toenhance the exploration and exploitation capabilities of thegrasshopper optimization algorithm.At the same time, the cauchy mutation is added to the best grasshopper to increase the ability to jump out of local optima.Finally, six benchmark functions are selected for testing. Experiments show that the improved algorithm has fast convergence and high precision.