## 1 Introduction
In another work by [PERSON], [PERSON] [56], the author also used the GWO to maintain the induction motor's speed regulation since the PI controller showed very poor performance at many points of the drive. The result indicated that the GWO works efficiently with the tuned controller's response compared to the traditional PI controller. [PERSON], [PERSON] [57] used the GWO to optimize the PID controller to control a ball's position in a magnetic levitation system (MLS). The result shows that the GWO can provide a good tuning on the parameter while minimizing the index system's performance. The response of the time and frequency domains was improved. In [PERSON] [58], the optimal PI controller parameter for doubly-fed induction generator (DFIG) based wind turbine was searched interactively by the grouped GWO (GGWO) proposed by the author. An adjustment was made on the existing GWO by expanding the pack's hierarchy group into two independent groups to broaden the grouping mechanism. The working of the GGWO made this method functioning twice as better compared to the basic GWO. As the results, the GGWO was able to extract and achieve optimal wind energy. [PERSON] and [PERSON] [59] investigated the use of the GWO in minimizing the error between the forecast and the exact price of the energy commodity for short-term series for crude oil and the cost of gasoline. The finding showed that the GWO can produce an outstanding result by making better gasoline and crude oil forecasting than the ABC. [PERSON], [PERSON] [60] used the GWO to find the optimal energy consumption of energy sources and raw materials. The result shows that the GWO gave a better performance in terms of speed and global value. [PERSON] and [PERSON] [61] use the GWO to forecast the future of gold prices, which minimizes the investment's risk. It is found that the GWO performed well in predicting feature selection of chosen gold price. [PERSON] and [PERSON] [62] proposed a hybrid GWO algorithm with the lion algorithm. In contrast to traditional GWO, the LI-GWO required a searching task and need to calculate fitness for every situation and place. The result shows that the proposed method can provide better performance in lateral inhibition of image pre-processing. [PERSON] and [PERSON] [63] suggested genetic GWO using several sensors' images, lowering the input image data. The result of GGWO was compared with GA and PCA, and it was found that the GWO gave better visual quality for performances and achievements. An adaptive GWO was proposed by [PERSON] and [PERSON] [64] for finding the optimum location of partial discharge (PD) source locations. Some sensors based on the emission techniques were applied for PD detection. Parameter dependency, step size, or position towards an optimal solution to achieve local minimum avoidance and fast convergence speed were the adaptive methods included in the structure. The finding showed that the PD source was easy to use and can provide an outstanding performance than traditional GWO, GA, PSO, linear PSO. [PERSON], [PERSON] [65] used the GWO algorithm for solving the two-dimension path planning problems of an unmanned combat aerial vehicle (UCAV). The UCAV could find a safe route while avoiding obstacles with minimum fuel usage. The performance of the GWO was compared with other metaheuristics algorithms to validate the effectiveness of the GWO. The result showed that the GWO can provide a better understanding than others. This is due to the high level of local optima avoidance capability possess by GWO. In addition, GWO also can enhance the effort for finding the proper prediction of an optimum weight sum. [PERSON] and [PERSON] [66] proposed a hybrid GWO with the Taguchi method for optimizing the multi-pass milling process. The main parameters of the GWO were tuned by using the Taguchi method to find the best solutions without trapping local minima. The Taguchi method was used to find the optimum solutions of the GWO's parameters.