*2.2. Gray Wolf Optimization*

Gray wolf optimization (GWO) is a swarm-based optimization algorithm inspired by the predation behavior of wolves [41]. Compared with other traditional intelligent swarm algorithms, GWO has the advantages of fewer parameters, easy implementation, great convergence speed, and global search ability, so it has been widely used in many fields [42]. Through observation, it is found that wolves hunt mainly in three parts: first tracking, chasing, and approaching prey; then surrounding and harassing prey from all directions until it stops moving; and finally, attacking the prey. Figure 2 shows the process of GWO, *a* is based on a linear decrease iteration convergence factor, and *A* is the value in the interval [−2*a*, 2*a*], by setting the |*a*| < 1 or > 1 to implement the prey. *C* can be arbitrarily set in the interval [0,2], indicating the weight of prey affected by the position of the gray wolf. α, β and δ represent the potential superior solution of the optimization objective, where α is the optimal solution, β is the suboptimal solution, and δ is the third optimal solution.

**Figure 2.** The process of GWO.
