A Modified Grasshopper Optimization Algorithm

Abstract

In order to solve the problems of Grasshopper Optimization Algorithm (GOA) in the optimization process, such as easily falling into the local optimum and the slow convergence speed, a Modified Grasshopper Optimization Algorithm (MGOA) is proposed. And the proposed MGOA improves the original fixed parameters to linear decreasing parameters in order to avoid the original algorithm from falling into the local optimum problem. At the same time, weighting calculation is introduced to improve the optimization accuracy of the MGOA algorithm. Additionally, the proposed MGOA is combined with the Simulated Annealing (SA) to improve the convergence speed of the original GOA algorithm. Both the unimodal test function and the multimodal test function are respectively used for simulation experiments, and experimental results show that, compared with the other five swarm intelligent algorithms, the proposed MGOA has significant advantages in aspects of accuracy, stability and convergence speed.