Improved Harris Hawks Optimization Algorithm

Abstract

ABSTRACT The Harris Hawks Optimization (HHO) algorithm is a nature‐inspired metaheuristic that mimics the cooperative hunting behavior of hawks. Despite its success in various optimization tasks, it suffers from several limitations, including low computational accuracy, a tendency to become trapped in local optima, and difficulty in balancing exploration and exploitation. To address these challenges, this paper proposes an enhanced version of HHO, named FL‐HHO, which integrates four key improvements: the Halton sequence for enhanced population diversity, a modified Escaping Energy Factor E, an improved Frog‐leaping mechanism, and a convergence trend analysis module. FL‐HHO is evaluated on seven classical benchmark functions and 30 functions from the CEC2014 benchmark suite. The experimental results demonstrate that FL‐HHO exhibits a significant advantage on classical benchmarks, achieving top performance in search precision across nearly all functions and reaching the theoretical optimum on three of them. In terms of computational efficiency, FL‐HHO ranks third among all compared algorithms. On the CEC2014 benchmarks, it secures first place on over 50% of the functions, with slightly lower performance observed on certain multimodal functions. Ablation experiments further verify the effectiveness of each proposed component, particularly highlighting the contribution of the modified Frog‐leaping mechanism to global exploitation and the Halton sequence to initialization robustness. In practical scenarios, FL‐HHO is applied to industrial robot path planning, where it achieves the shortest travel distance among all evaluated methods, confirming its effectiveness in real‐world tasks. The implementation code is publicly available at: https://github.com/zhu‐cheng/FL‐HHO/tree/main .