# A Novel Hybrid Harris Hawk-Arithmetic Optimization Algorithm for Industrial Wireless Mesh Networks
## 1 Introduction  
Network Control Systems (NCS) have become increasingly popular in mining, process, and manufacturing industries that produce various goods such as food and beverages, different metals, multiple chemicals, pulp and paper, automobiles, textiles, crude oil refineries, and power generation plants [1]. NCS monitors and controls field instruments effectively, ensuring efficient and accurate operations [2]. In recent times, WMN has gained widespread acceptance in the industry, particularly with the wide acceptance of different wireless networking protocols, namely WIA-PA, Zigbee, WirelessHART, Bluetooth, and ISA 100 wireless. One of the primary advantages of these wireless standards is eliminating clunky cabling--a costly and time-consuming process to repair and maintain [3, 4]. Additionally, WMN enables the expansion of network capability to the regions where it is difficult or impossible to install the wired cables, thus making monitoring and controlling operations in remote locations possible. Moreover, using WMNs increases data reliability, reduces data loss or interference, and offers improved security measures, protecting sensitive data from unauthorized access or theft [5]. It is essential to have a thoroughknowledge of several factors such as transmission power, network topology, and wireless node density. Ignoring these factors while deploying WMN routers can lead to inadequate client coverage and connectivity, substandard network range, control-loop failures, and transmission loss [6]. It is crucial to consider the technical constraints of the deployment location and the basic topology before deploying wireless mesh routers. These constraints include factors such as radio signal strength, frequency range, and interference from other wireless networks [7; 8]. WMNs offer many benefits for all the process control and automation industries [9]. However, some issues must be addressed to enhance the network's performance, including network coverage, router connectivity, network traffic, compatibility, data security, etc. While comparing the outdoor regions, the indoor area is challenging while implementing the WMN for process monitoring and control because of the stochastic interferences [10]. In some outdoor areas, installations require fewer clients than in indoor regions. Furthermore, WMN can be implemented in multiple places with the availability of various control services in indoor and outdoor conditions. Determining the appropriate location for installing WMN routers for real-world applications is crucial. Therefore, optimization is essential for ensuring reliable and efficient WMN performance [11]. Optimization involves fine-tuning various parameters, such as router placement, transmission power, network topology, and routing protocols, to achieve the best possible network performance [12]. Optimizing these parameters allows the network to ensure that the WMN provides seamless coverage, reliable connectivity, and low latency, even in challenging industrial environments. Additionally, optimizing industrial WMNs can help improve network scalability and reduce maintenance costs [13]. At the same time, a feedback controller is essential in WMNs to ensure the system's stability. In addition, the feedback controllers will help the system to maintain stable and accurate control over the process variables [14]. However, to achieve optimal performance, the controller must be optimized based on the system characteristics, process model, and external disturbances of the system it controls. Optimization techniques such as trial and error, Ziegler-Nichols, and the Cohen-Coon methods are commonly used to optimize feedback controllers. These conventional techniques involve adjusting the controller's parameters to find the optimal values that result in suboptimal, unstable, and ineffective control. Thus, many researchers proposed using metaheuristic-based techniques to optimize the controller parameters for different applications [15; 16; 17; 18; 19; 20].