@article{MAKHILLIJSC20127221070,
    title = {Influence of Search Algorithms on Aerodynamic Design Optimization of Aircraft Wings},
    journal = {International Journal of Soft Computing},
    volume = {7},
    number = {2},
    pages = {79-84},
    year = {2012},
    issn = {1816-9503},
    doi = {ijscomp.2012.79.84},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2012.79.84},
    author = {R.,R.,U. and},
    keywords = {Aerodynamic shape optimization,parametric section,particle swarm optimization,genetic algorithm,geometry,design},
    abstract = {The method of search algorithms or optimization algorithms is one of the most important parameters which will strongly influence the fidelity of the solution during an aerodynamic shape optimization problem. Now a days various optimization methods such as Genetic Algorithm (GA) and Simulated Annealing (SA), Particle Swarm Optimization (PSO), etc. are more widely employed to solve the aerodynamic shape optimization problems. In addition to the optimization method, the geometry parameterization becomes an important factor to be considered during the aerodynamic shape optimization process. Since, the reduction in the number of design parameters is one of the most important requirements for the aerodynamic shape optimization problem, it becomes important to mathematically describe the airfoil geometry with minimum number of design parameters. The objective of this study is to introduce the knowledge of describing general airfoil geometry using twelve parameters by representing its shape as a polynomial function and coupling this approach with flow solution and optimization algorithms. It is also demonstrated that the estimation of a suitable optimization scheme for a given optimization problem. An aerodynamic shape optimization problem is formulated for NACA 0012 airfoil and solved using the methods of Particle Swarm Optimization and Genetic Algorithm for 5.0 deg angle of attack. The results show that the particle swarm optimization scheme is more effective in finding the optimum solution among the various possible solutions. It is also found that the PSO shows more exploitation characteristics as compared to the GA which is considered to be more effective explorer.}
    }