## 4 GWO's Functions in the Distribution Network
It has been determined that the distribution network would be divided into three zones to provide charging facilities in different portions of a particular geographic area. In each of the three sites, a charging station has been placed to facilitate the use of electric vehicles. To evaluate whether the issue is an optimization problem, researchers used the GWO and the whale optimization algorithm (WOA) to investigate the situation. Additionally, the GWO method was made to deal with the problem of changing the way feeders are set up in power distribution systems [64]. The method makes use of a fitness function that corresponds to the optimal combination of switches in power distribution systems to address the issue of feeder reconfiguration in power distribution systems. For the reconfiguration problem to be solved successfully, it is important to simulate an objective function, which requires reducing the amount of real power lost. An algorithm for grey wolf optimization that is inspired by nature was used to restructure the power distribution system and discover the optimal switches that would result in the least amount of power loss throughout the distribution network [65]. The algorithm was designed to find the best switches for the least amount of power loss in the distribution network. Regarding smart grids, the reconfiguration of distribution networks (DNs) is being intensively researched to offer fault tolerance as well as the ability to quickly revert to an adaptable, dependable distribution network. Radiality is one of the most important parts of DN topology, so finding a good way to set up DN is an NP-hard optimization task. A DN is a distributed network with many nodes. This has resulted  
Figure 5: The distribution network (DN) reconfiguration. in the evaluation of the current manta ray foraging optimization (MRFO) approach in this context for the solution of comparable DN optimization challenges. Particle swarm optimization (PSO) and GWO are two common optimization methods that are used to compare the performance of MRFO and GWO. It is possible to study multiple operating scenarios for both the IEEE 33-bus and the IEEE 85-bus systems with the use of these optimization approaches, which are described below. A realistic reconfiguration of a DN that has the least amount of power loss while maintaining the optimum improved voltage profile is what we are trying to discover here. Based on the results of the simulations, the proposed MRFO approach provides efficient and remarkable performance in a wide range of operating conditions. This method has been proven to be efficient and long-lasting. Depending on the situation, the power loss reduction ratio is between 21% and 41%, and the voltage profile has been raised enough [66]. Multi-criteria optimization may be used to modify the distribution network to find the best design. Low-cost and technological methods for improving network attributes are discussed here. Unbalanced distribution networks can be redesigned to minimize power loss, voltage unbalance, voltage sag, and energy not supplied by customers while also improving voltage sag and using a new, improved coronavirus herd immunity optimizer algorithm. These methods are currently emerging. In systems to build up and scale distribution network systems, they beat GWO and HBBC, two other algorithms now under investigation, in terms of output quality. This is a reliable strategy [67; 68]. People use grey wolf optimization and particle swarm optimization methods to cut their active power loss and make more money. IEEE 33- and IEEE 34-bus distribution systems are combined with these methods. Furthermore, the predicted strategy yields the same conclusion as the conventional GWO. This method, called the stochastic GWO approach, has been used to charge electric cars at the lowest possible cost [69]. It is possible to simplify complex multi-objective optimization problems by using this technique.