The Harris hawks optimization algorithm (HHO) is a novel swarm-based meta-heuristic algorithm. In this study, a modified Harris hawks optimization algorithm (MHHO) is proposed to enhance the searching performance of the conventional HHO. Past studies have revealed that different adjustment strategies of important variables in meta-heuristic algorithm will evidently affect the performance of the algorithm. Therefore, this study focuses on the escaping energy (E) of prey is an extremely, which is a critical concept that determines the balance between the exploration and exploitation phases of the HHO. In nature, the Harris hawks will take different the perch strategy and the chasing pattern according to E. For E, six different update strategies are designed to model the real situation. To explore the differences between the six strategies mentioned above, a comparative study through twenty representative benchmark functions is carried out by Experiment 1 (Sect. 4.2). The results show that strategy 6 (the exponential decreasing strategy) outperforms other rivals; therefore, it is deployed into the MHHO. To further demonstrate the superior search performance of MHHO, a similar comparative study between MHHO and several well-established optimization technologies is carried out by Experiment 2 (Sect. 4.3). The results clearly exhibit MHHO outperforms its rivals in most benchmark functions. In addition, compared with other well-known optimizers and the conventional HHO, the competitive results obtained by MHHO on two engineering optimization problems also prove the effectiveness and superiority of the proposed MHHO in solving constrained optimization problems.

哈里斯霍克斯霍克斯优化算法（HHO）是一种新颖的基于群体的元启发式算法。在这项研究中，提出了一种改进的哈里斯霍克霍克斯优化算法（MHHO），以提高常规HHO的搜索性能。以往的研究表明，元启发式算法中重要变量的不同调整策略显然会影响算法的性能。因此，本研究关注的是猎物的逃逸能量（E）是极端的，这是决定HHO勘探和开发阶段之间平衡的关键概念。本质上，哈里斯鹰将根据E采取不同的栖息地策略和追逐模式。对于E，设计了六种不同的更新策略来对实际情况进行建模。为了探索上述六种策略之间的差异，实验1（第4.2节）通过二十种代表性基准函数进行了比较研究。结果表明，策略6（指数递减策略）的表现优于其他竞争对手。因此，将其部署到MHHO中。为了进一步证明MHHO的卓越搜索性能，实验2（第4.3节）进行了MHHO与几种成熟的优化技术之间的类似比较研究。结果清楚地表明，MHHO在大多数基准测试功能上均优于竞争对手。此外，与其他知名的优化器和常规的HHO相比，