## 2 Theory for UAV's Wing Structure Optimization
### Optimization Procedure Using a Genetic Algorithm  
#### 2.3.1 Stacking Sequence for the Composite Structure  
To better understand the concept of describing the stacking sequence using the stacking sequence table, the concept of guide-based mixing should first be described. In variable stiffness designs, there is generally an appropriate number of panels constituting a certain rigidity and stacking order accordingly. The process of dropping or adding layers between adjacent panels is called mixing and is described in Figure 3. In the case of internal and external mixing, ply drops are allowed in sequential order from outside to inside or vice versa. Another type of mixing can be found in Figure 3b. Panels I to II are composed of generalized mixing of all piles from thin to thick layers continuously, and panels II to III are composed of thick lamination and simple mixing. Two mixing types can be combined with a guided approach. In this paper, we show the stacking of a single fly, which may contain at least the thickest panel, according to consideration, and the mixing of Figure 3a can be effectively explained using guidance. Following the instructions of each panel, the stacking order is described by defining the fiber angle in the guide, with information about the number of ply drops per panel. Figure 1: General operation of the genetic algorithm. Figure 2: Selection, crossover, and mutation. The general genetic algorithm contains three operators: selection, crossover, and mutation. The selection operator selects chromosomes from the object group for reproduction, as shown in Figure 4c. The chromosomes selected are based on their suitability, and higher suitability results in a large group of fit individuals, and a lower fit gives them a long evolutionary time. The selection aims to reproduce objects close to the fitted values, so the fitted values should be selected following change and balance. The crossover operator produces two offspring by exchanging some of the before and after arrays of random locations in the selected chromosome. For the optimization of composite material structures, the genetic algorithm was implemented by FORTRAN, and the developed optimization code allowed the user to set design variables, limiting functions, and initial values and consisted of three main components. First, data files that set inputs, such as design variables, limiting functions, repeatability, initial values to perform optimization, and design optimization tools  
Figure 3: Schematic overview of a GA stacking sequence optimization in combination with approximations. developed to solve various optimization problems, are available for linear and nonlinear optimization problems, regardless of the constraints or not. Second, factors within the range of design variables are randomly selected, and the upper factors that best match the objective function are selected and converged through generation. Because we randomly screen the stacking angles, we can find the overall minimum of the optimization problem. Lamination thickness optimization is performed to obtain a minimized mass satisfying global and regional limiting conditions. The convergence speed is also fast and performs similarly to the most preferred optimization algorithm. The genetic algorithm program code was implemented using FORTRAN, and an optimization analysis was performed. In genetic algorithms, mass is defined for minimization, and applying the same load case and stiffness optimization code applies the same constraints for deformation and buckling. Figures 3 and 4 show the overall optimization process. First, primary stiffness conditions should be met using genetic algorithms. The optimized framework then maximizes the stiffness function by changing the stacking angle. The factors within the scope of the design variables were selected at random, and the top factors that best matched the objective function were selected and converged over the generation. The random selection of stacking angles allowed us to find the overall minimum of optimization problems.