## 1 Introduction  
An unmanned aerial vehicle (UAV) is an aircraft which is guided by remote control and can be autonomous. A UAV is also known as drone which does not have a human pilot or any passengers on it [1]. Manned aircrafts have benefited the possibility of switching to automatic pilot, which is not a fully autonomous system, for decades. The main difference between manned aircraft and UAVs is full flight autonomy [2]. The advantages of UAV systems compared to manned aircraft is the capability to perform dangerous and complex missions with lower costs. A quadcopter is a UAV that consists of four rotors. A quadcopter has brushless motors, electronic speed controllers, a flight controller board, communication equipment, and a lithium polymer battery [3]. A quadcopter has vertical take-off and landing capabilities. A quadcopter is a structurally simple vehicle; however, its control system is complex because of its nonlinear dynamic behavior [4]. A quadcopter UAV has only tens or hundreds of components, while a classic aircraft starts from 1.5 million components upwards. The quadcopter is even simpler than a car, which today has about 15,000 different components. Its simple, light, and cheap structure and remote controlledand autonomous navigation capability are great advantages compared to classic aircraft. Some drones that transport heavy materials and or passengers may have many more components (tens or hundreds of thousands), but they are still structurally simpler than a classic aircraft. The literature over the last two decades has been focused on the development of new control methods, path planning, and developing new UAV designs. In the literature, the control and stability of quadcopters have been investigated in many studies [5]. Researchers have focused on different research on dynamic models and the control of quadcopter UAVs [6]. In the control of quadcopters, one of the most preferred controllers is the proportional-integral-derivative (PID) controller [7]. PID-type controllers have been used in many applications that require accurate control. In the PID controller, error value is calculated by using the difference between the setpoint and the measured value of the output of the system. Fractional order PID (FOPID) controllers are used in many studies including quadcopter control [8; 9]. Optimal control is a type of mathematical optimization that includes providing the best conditions for a dynamic system. Optimal controllers, especially PID-based ones, have been used in numerous applications in engineering and science. Optimal PID controllers provide one of the best solutions for quadcopter control, since they have a nonlinear dynamic structure and are an unstable system. The parameters of PID controllers are usually mathematically adjusted with optimization algorithms. Optimization methods aim to find the optimal conditions of an engineering system. Optimization can be defined as finding the optimal ones from a collection of possible solutions. Meta-heuristic (MH) algorithms are optimization algorithms that are classified under stochastic algorithms. MH algorithms are a large branch of optimization algorithms that provide near-optimal results for solving large-scale optimization problems in many research fields. MH algorithms became effective tools to optimize engineering problems [10; 11]. In the literature, MH algorithms made significant contributions to the solution of many engineering problems. There are many recently developed MH algorithms. MH algorithms belong to a large family, but most MH algorithms are swarm-based. In recent years, the number of single usage and hybridizations of MH algorithms has increased in the literature [12]. Fire Hawk Optimizer (FHO) is an MH algorithm that mimics the foraging behavior of whistling kites, black kites, and brown falcons in nature to solve optimization problems. These birds are called fire hawks because they are capable of setting fires for hunting [13].