## 3 Related Work  
[PERSON] (2008) investigated the GA, a metaheuristic algorithm based on natural selection and generational reproduction of the fittest humans. Initialization, fitness, selection, cross-over, and mutation are the phases of the GA algorithm. Two extensions of GA are adaptive genetic algorithms and coarse-grained parallel genetic algorithms. They are utilized for a variety of tasks, including feature extraction, its sub-set selection, engineering designs of CAD, and the traveling salesman dilemma for optimization. The two most essential elements that determine the algorithm's efficiency are the fitness function and the number of iterations. By combining the parents you have chosen, you will be able to create new genetic algorithms from the current generation (often referred to as parents) to generate offspring in the subsequent generation. They have the advantage of being able to achieve faster convergence, have a simple implementation procedure, and are optimized for a wide range of functions. They have several drawbacks, such as a proclivity for concentrating on local optima rather than global optima. For decision-making issues, GA is inefficient and ineffective. [PERSON] (2018) studied the foraging technique of ant species used in ACO. The ants leave a pheromone, a route marker that may be followed by others, to indicate a good path. The constructive greedy heuristic approach for finding excellent pathways through networks eliminates issues. Authors use a simple phenomenon by using ants' pheromones as their path tracker by which they interact with each other. Edges and node weights are dynamically updated by the agent using the random probabilistic pheromone-based model. The used algorithm continuously repeats iterations to keep updating the path. Multiple paths are created from which the optimum path is to be selected. [PERSON] (2010) studied an Artificial Bee Colony (ABC) algorithm, another metaheuristic algorithm that includes a food source, jobless, and employed FB. Honey bees benefit from forager feedback on food sources because it allows them to adapt and share knowledge. Employed, scout, and spectator bees are the three categories of bees. The food supply is the same as a feasible solution. The amount of nectar calculated is proportional to the solution's fitness in this algorithm. A specific operation is performed just once for a single unit of scouts, hired, and onlookers, and similarly for the other categories. [PERSON] (2010) studied a population-based evolutionary algorithm that, hypothetically and repeatedly, improves any mathematical function. It also improves the candidate solutions in terms of fitness function and provides a quality control technique. It is easily able to break out from local optima and obtain a fast convergence rate. [PERSON] (2011) implemented an intelligent water drop (IWD) robust algorithm for fast convergence to the global optimization problem in WSN. In IWD, each water drop has a velocity and soil present in this journey. The velocity and the soil are determined by the quantity of soil and the time it takes to traverse the field, respectively. In its course, an IWD always favors low soil content. Each IWD passes through it, producing the best solution that is utilized to update the global best solution regularly. [PERSON] (2012)presented a Gravitational Search Algorithm (GSO) in which the active gravitational mass (GM), inertial mass (IM), location, and passive GM of each object are listed. GM and IM regulate the velocity of an object. The software navigates by modifying the mentioned masses until all of the masses are drawn to the heaviest mass, which is often considered the best choice. [PERSON] [14] presented a Bat Algorithm (BA) in which bats change their wavelength and rate of emission depending on how close they are to their prey. Echolocation is a method that is utilized to figure out where they are. They can tell the difference between loudness and the intensity of a pulse that should be within a specified range.