Grouped Grey Wolf Optimizer with differential evolution for global Optimizer and test scheduling for Support vector regression

This paper presents a new approach to optimization, called the grouped grey wolf optimizer (GWO) with differential evolution. The proposed structure divides the grey wolves into two groups: the cooperative hunting and the random scout group. The hunting group is responsible for exploring unfamiliar environments and exploiting deeper search spaces, while the scout group conducts a global search using the differential evolution algorithm to prevent the optimizer from getting stuck in local optima. This approach strikes a balance between exploration and exploitation, resulting in improved convergence. There are three case studies that demonstrate a better global convergence.