An Adaptive Average Grasshopper Optimization Algorithm for Solving Numerical Optimization Problems

Abstract

The grasshopper optimization algorithm (GOA), inspired by the behavior of grasshopper swarms, has proven efficient in solving globally constrained optimization problems. However, the original GOA exhibits some shortcomings in that its original linear convergence parameter causes the exploration and exploitation processes to be unbalanced, leading to a slow convergence speed and a tendency to fall into a local optimum trap. This study proposes an adaptive average GOA (AAGOA) with a nonlinear convergence parameter that can improve optimization performance by overcoming the shortcomings of the original GOA. To evaluate the optimization capability of the proposed AAGOA, the algorithm was tested on the CEC2021 benchmark set, and its performance was compared to that of the original GOA. According to the analysis of the results, AAGOA is ranked first in the Friedman ranking test and can produce better optimization results compared to its counterparts.