Development and evaluation of hybrid harris hawks optimization algorithms for advanced engineering applications.

Harris Hawk Optimizer (HHO) is a recent revolutionary algorithm developed in the literature that simulates the cooperative hunting behaviour of Parabuteo Unicinctus. Despite its simplicity, the standard HHO often suffers from slow convergence, limited exploitation capacity and performance degradation on high-dimensional and constrained problems. This study aims to develop seven novel Harris Hawk Optimizer (HHO) variants, HHO-ADAP, HHO-CHAOS, HHO-Elite, HHO-GA, HHO-Inertia, HHO-PSO, and HHO-ULTRA, that integrate adaptive mechanisms, chaotic dynamics, elite preservation, and cross-algorithmic hybridization to improve the balance between exploration and exploitation. The proposed methods were rigorously tested on the CEC 2014 benchmark suite for dimensions 10, 30, 50, and 100, as well as ten constrained engineering design problems, and results are compared against state-of-the-art optimizers CMA-ES, L-SHADE, LSHADE-cnEpSin, SPS-L-SHADE-EIG, EBOwithCMAR, WMA, and OWMA. Quantitative results demonstrate that the hybrids consistently outperform the baseline HHO and classical optimizers. HHO-PSO and HHO-Elite achieved up to 35% faster convergence and reached solution values as small as 10⁻