## 1 Introduction
### Literature Survey
The \"Morphing Architectures and Related Technologies for Wing Efficiency Improvement-CRIAQ MDO 505\" was also realized at the LARCASE in the continuation of the CRIAQ 7.1 project mentioned above. The achievements of the international Canadian-Italian CRIAQ MDO 505 project are mentioned in various publications [45; 46]. The aircraft optimization process has evolved dramatically over the past decades. The design of new intelligent algorithms and computational solvers has substantially impacted the overall design process, including the \"morphing aircraft optimization\". The verification and validation of aircraft design using optimization techniques has reduced its huge experimental costs and has been achieved by the use of efficient algorithms and computational solvers [47; 48]. An optimization process is initiated by minimizing or maximizing an objective function for the target design concept with respect to design variables subjected to given constraints, which were applied to limit the search space and therefore to yield physically feasible optimization results. Both gradient-based and gradient-free intelligent algorithms have been used in these optimization processes, and their results are dependent on the type of optimization problem to be solved [47]. These algorithms are discussed in the following sections. Many optimization techniques based on bioinspired processes and Surrogate-Assisted natural methods have been formulated,and mathematical and statistical analysis of these algorithms has been performed [49; 50; 51; 52]. The comparative performance study based on convergence trends was performed with different geometry parameterization techniques. Both advantages and disadvantages can be found in these optimization algorithms for complex shape functions. However, the goal of this study is to perform aerodynamic design optimization of morphing airfoil using Particle Swarm Optimization (PSO) algorithm combined with Pattern Search technique. Similarly, various computational solvers are employed based on their desired solution accuracy and computational cost [53]. Researchers have conducted various aerodynamic and structural optimization studies, including design studies of different surfaces (components) of the wing, such as its upper surface, trailing edge or leading edge. Adjoint-based airfoil optimization has been studied using Euler equations [54], while other researchers have implemented extensive gradient-based aerodynamic shape optimization methodologies for transonic wing design using high-fidelity RANS solvers [55]. Many other optimization studies have been performed using different optimization techniques. Some of the most notable works are summarized in Table 1.  
The study presented in this paper analyses the aerodynamic optimization of the Hydra Technologies S45 Balaam UAS airfoil. This unmanned aerial surveillance (UAS) system is needed to provide security and surveillance capabilities for the Mexican Air Forces, as well as civilian protection in dangerous situations. This study of Morphing Wing Technology has the aim to enhance the aerodynamic efficiency and the effective range of the UAS S45, as well as to extend the flight time. The next section of this paper presents the optimization framework of the overall methodology, which includes two objective functions' formulations (\"drag minimization\" and \"endurance maximization\"). The parameterization technique is also presented in Section 2 and has the aim to obtain the \"optimal aerodynamic shapes\"; the computational solvers for calculating aerodynamic coefficients and the optimization algorithms employed are also presented. The results obtained by these solvers and algorithms for the UAS-S45 optimized designs are compared with the baseline UAS-S45 model results.