A new improved optimizer adjusted by elite adjustment strategy

Abstract

A modified Grey Wolf Optimization (CGWO) algorithm is introduced, employing a cosine improvement factor strategy to address the challenges of slow convergence speed and low optimization accuracy encountered in the initial Grey Wolf Optimization (GWO) algorithm. This approach aims to enhance the efficiency of the GWO algorithm by integrating a cosine function-based improvement factor strategy. The optimization performance of CGWO is compared with that of several classic swarm intelligence algorithms across four typical test functions. The results of experiments indicate that CGWO shows superior convergence speed, optimization accuracy, and stability when compared to three other classic swarm intelligence algorithms.