# A Novel Hybrid Harris Hawk-Arithmetic Optimization Algorithm for Industrial Wireless Mesh Networks
## 1 Introduction
### Arithmetic Optimization Algorithm
and passed over to the next generation, allowing the algorithm to explore different areas of the solution and converge to the global minimum efficiently. [PERSON] et al. [42] proposed a newly developed approach called Multi-Objective AOA (MOAOA) to solve real-world constrained multi-objective optimization problems. The latest version of the AOA algorithm has been improved by adding two new functions--elitist non-dominance sorting and a crowding distance-based mechanism. This new algorithm has been tested on 35 optimization problems with constraints and five test problems without constraints. Its effectiveness has been compared with four other advanced multi-objective optimization techniques. Additional performance metrics are also considered to evaluate the MOAOA for higher accuracy and effective convergence, in which MOAOA achieved superior performance. In [43; 44; 45], the researchers developed an enhanced form of artificial neural network which uses a two-step process to identify, locate, and measure the extent of damage in plate structures made of functionally graded materials, petroleum products, and other electrical systems. In the first stage, a damage indicator based on the frequency response function is utilized to forecast which components of the material have been affected. In the second stage, the networks are employed to quantify the damage and it eliminates healthy elements from the numerical model and utilizes information from the defective components to estimate the degree of damage. [PERSON] et al. [46] proposed a new method for various multilevel thresholding in data analysis and image segmentation using the AOA and differential evolution technique. The DAOA algorithm was assessed using traditional examination images from nature and CT COVID-19 images. The accuracy of the segmented images was measured using the peak signal-to-noise ratio and structural similarity index test. [PERSON] et al. [47] developed a new hybrid algorithm called DESMAOA, combining two meta-heuristic algorithms--the Slime Mold Algorithm (SMA) and the AOA. The DESMAOA algorithm was designed to improve the optimization capability of the existing techniques. The SMAOA algorithm is first used to improve the SMA algorithm, and then two strategies from SMA and AOA are combined to create the DESMAOA. In [48], a new method is developed to control an automatic voltage regulator using a robust Model Predictive Controller (MPC). The suggested approach was crafted to address the challenge of uncertain automatic voltage regulation parameters. Frequency domain conditions are obtained through the Hermite-Biehler theorem to ensure stability in the face of perturbations. The tuning of MPC parameters is performed using the AOA technique while accommodating stability constraints. A time-domain objective is established to optimize the voltage regulator's performance by minimizing voltage peak overshoot and fastening the process settling. In [49], the authors introduced a forced switching mechanism for AOA termed IAOA that helps search agents to switch between various local optima. The effectiveness of the IAOA was tested on multiple benchmark functions and real-world test problems and the results showed that it outperforms other optimization algorithms in most cases. Different types of metaheuristic optimization were compared for the various engineering problems in [50]. The researchers proposed an enhanced version of the AOA called nAOA. The nAOA technique uses logarithmic and exponential mathematical operators to improve algorithm performance. The overall summary of the AOA technique is given in Table 2.  
Some of the main contributions of this research article are listed as follows:  
1. The primary motivation of HHAOA is to solve the problem of placing the routers optimally to achieve adequate coverage and connectivity, along with finding the best parameter for the Fractional-Order Predictive PI (FOPPI) controller. 2.