Hybrid Harris Hawks Optimization with Ant Lion Optimization Algorithm for Mathematical Intelligence Systems: A Computer Vision Approach
DOI:
https://doi.org/10.29304/jqcsm.2026.18.32905Keywords:
Numerical Optimization, Metaheuristic Optimization, Hybrid HHO-ALO AlgorithmAbstract
This research is a comparative study of Harris Hawks Optimization (HHO), Ant Lion Optimization (ALO) and Hybrid HHO-ALO algorithm to solve Computer Vision (CV) Approach problem as a complex numerical optimization problem. The proposed hybrid algorithm marries the best aspects of both algorithms, namely good exploitative performance of ALO and the good exploration performance of HHO, while nominally addressing the drawback of each algorithm: premature convergence and slow convergence near the global optimum. To assess the performance of the algorithms in terms of convergence speed, robustness and solution accuracy, some well-known benchmark functions have been used, including Sphere, Rastrigin, Ackley, Rosenbrock and Griewank. The simulation results show that the hybrid HHO-ALO algorithm can achieve better convergence speed and reduce the mean square error (MSE) than single HHO and ALO algorithm under various optimization landscapes, and the algorithm has better optimization performance than any other algorithm no matter which landscape. Furthermore, the hybrid approach proves itself better suited for dealing with multimodal, non-convex and trickily designed search spaces, as it seems to be very effective in escaping local minimum and thus ensuring more stable performance in optimization. Based on the findings of this study, HHO-ALO can be an effective and robust optimization algorithm to be used in complex engineering and computational problems, which further shows the potential of using the hybrid optimization algorithm when the adaptive optimization can be constructed and adapted to real constraints.
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