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Springer Handbook of Computational Intelligence
A significant challenge to the application of evolutionary multiobjective optimization (EMO) for transonic airfoil design is the often excessive number of computational fluid dynamic (CFD) simulations required to ensure convergence. In this study, a multiobjective particle swarm optimization (MOPSO) framework is introduced, which incorporates designer preferences to provide further guidance in the search. A reference point is projected onto the Pareto landscape by the designer to guide thedoi:10.1007/978-3-662-43505-2_67 fatcat:3cz54dknl5el5fwtnmkrn6cbje