## 7 Overview of Algorithms for Energy Optimization UAV-Based Cellular Networks
### Swarm Intelligence-Based Algorithms  
These algorithms are inspired by nature and designed based on the relationship between living organisms, including ants, birds, bees, flocks of fish, bacterial communities, etc. [130]. In this regard, Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) and considered in the following paragraphs as representative of swarm intelligence-based algorithms. * Particle Swarm Optimization (PSO) Algorithms:The PSO algorithm was introduced by Eberhart and Kennedy in 1995 [131]. It was developed as a population-based optimization method inspired by the two-dimensional behavioral movement of bird and fish flocks in nature. It has a more straightforward computation method than other traditional optimization methods and does not involve time-consuming complex operations. Therefore, it works faster, has shorter computation times, and is more preferred [132]. The solution approach of the PSO algorithm is as follows: There is a flock of birds in a region where there is only one source of food. Birds are randomly placed in this food area and no bird knows where the food is. However, the end of each iteration, they know how close they are to the food. In this case, it is a good decision to follow the bird closest to the food. PSO works according to this scenario and is used to solve optimization problems. Birds trying to find food in solution space are called \"particles\" in PSO. Each particle has a fitness value and velocity that enables it to fly. These are calculated using the fitness function. Particles fly out of the problem space, following the optimum particle at each iteration [133]. If there is no specific initial solution generation mechanism for a problem, the PSO is started with a group of random solutions (particle swarm) and tries to reach the global best solution with updates. The first obtained feasible solution value is kept as the best solution and the coordinates of the associated solution are determined. In each local search, this value is kept in memory for later use and is called the \"local best solution\". The other best value is the coordinates that provide the best solution ever obtained by all particles in the population. This value is kept as \"global best solution\". In each iteration, the local best solution is compared with the global best solution based on the objective function to develop the global best solution. * Ant Colony Optimization Algorithms (ACO): ACO is a meta-heuristic technique used for solving optimization problems and works based on swarm intelligence as PSO. It was developed by Dorigo et al. in 1991 and tested on different sizes of Traveling Salesman Problems (TSP). Dorigo named this algorithm the 'Ant System' [134]. The basis of this technique is the pheromone hormone that ants use in communication. Ants start the foraging process randomly, and when food is found, they secrete the pheromone hormone to show the other ants in the colony the pathway to the discovered food. This hormone is updated by other ants and helps the colony find the shortest path to food. An intense pheromone amount indicates the quality of the path and increases the probability of preference for the use of that path. If the ants encounter any obstacle on the way between the food and the nest, the ant in front of the obstacle cannot continue and they must make a decision for the new direction of the trip. Each of the new direction options is equally likely to be selected. If the ant chooses the shortest path, this path becomes the preferred route according to the pheromone hormone density. However, if the chosen path is not the shortest, the colony route is reconstructed very quickly and the amount of pheromone on the newly chosen path is increased to create a preference for the ants that come later. Considering that each ant releases the same amount of hormone at the same rate on average, the expected situation is that it takes a long time for the colony to recognize the obstacle and choose the shortest path.