# A Novel Hybrid Harris Hawk-Arithmetic Optimization Algorithm for Industrial Wireless Mesh Networks
## 1 Introduction
### Harris Hawk Optimization  
Initial development of the Harris Hawk Optimization, a swarm-based optimization technique, was proposed by [PERSON] et al. [24]. This method is motivated by the cooperation and hunting strategies of Harris hawks in nature, which involve team action and reaction, as well as prey evasion. The main objective of HHO is to discover solutions to single-objective problems. The HHO method uses hawks' chasing actions as search agents and the prey as the best position. This approach has the potential to solve a variety of real-world optimization problems, such as engineering design, pattern and speech recognition, manufacturing optimization, bio-mechanical engineering, power production quality, feature selection, and medical image segmentation [25; 26; 27; 28; 29]. As with other metaheuristic techniques, HHO uses exploration and exploitation phases to identify and catch the prey. However, the HHO differs from others in the exploitation phase, where it catches the prey based on soft and hard besiege and rapid dives on soft and hard besiege movements. Detailed information on the HHO algorithm can be found in these articles [24; 25; 28]. As a result of the benefits provided by HHO, there has been a significant increase in HHO research, with the number of studies continuing to grow rapidly to numerous applications and creating new HHO variations. The authors of [30] aimed to accelerate the convergence of the HHO by proposing an improved version of the algorithm that employs two strategies. The first strategy involves improving the exploration phase of HHO by incorporating opposition-based learning and logarithmic spiral techniques. The second strategy combines the Modify Rosenbrock method to enhance the HHO's local searching capability and convergence rate. The authors tested their algorithm by conducting experiments on 23 standard benchmark functions. They also compared their results with various state-of-the-art metaheuristic algorithms. In [31], two different variants of the HHO, namely binary HHO and quadratic binary HHO, were used to improve the feature selection problem at various data classification conditions. Binary HHO has a transfer function that can be either S-shaped or V-shaped, effectively converting a continuous variable into a binary one. The quadratic binary HHO is designed to improve the binary HHO by incorporating a binary quadratic model. [PERSON] et al. [32] developed a binary variant of the multi-objective HHO approach to address a classification task. Their novel discrete operators for exploitation (besieging) and exploration (perching) were introduced. Their study employed four machine learning methods applied to a COVID-19 dataset for binary classification problems to increase prediction accuracy. Reference [33] proposed a novel optimization algorithm, called the modified HHO, to reconfigure photovoltaic modules and disperse shadow regularly to increase the power generated. The algorithm was tested on different shade patterns with 9 \(\times\) 9, 6 \(\times\) 4, and 6 \(\times\) 20 photovoltaic arrays based on the available simulation data. The outcomes were analyzed and compared with varying techniques of reconfiguration based on metrics such as mismatch power, fill factor, power loss, and power enhancement. [PERSON] et al. designed an improved variant of the HHO named long-term memory HHO that enhances the diversity of search agents by maintaining a broad search region for the exploration phase resulting in better convergence results. The proposed technique uses different engineering optimization test problems, including power flow optimization for the power generation systems [34]. The improved HHO algorithm, as introduced by [PERSON] et al. [35], addresses the challenge of optimizing large-scale wireless sensor networks. The approach involves utilizing [PERSON]'s shortest path algorithm to establish minimum transmission paths from the sink node to all other sensor nodes in order to reconstruct the network.