GWO-HHO Hybrid: Strengthening Grey Wolf Optimizer with Harris Hawks Strategy for Numerical Optimization

Optimization algorithms play a crucial role in 
solving complex numerical problems across 
diverse domains. This paper presents a hybrid 
Grey Wolf Optimizer (GWO) and Harris Hawks 
Optimization (HHO) algorithm, designed to 
improve solution accuracy and convergence 
efficiency. 
The proposed hybrid approach 
leverages GWO’s structured leadership-based 
exploration with HHO’s dynamic and adaptive 
hunting strategies, ensuring a balanced trade- off 
between exploration and exploitation. The 
performance of the hybrid GWO-HHO algorithm

is  evaluated 
on 
twenty-three 
benchmark 
functions, and its results are compared with the 
original GWO. It is observed that the proposed 
hybrid approach achieves higher accuracy and 
improved optimization efficiency. In this paper 
GWO algorithm is combined with HHO 
algorithm for numerical optimization.