Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Real-Coded Adaptive Range Genetic Algorithm Applied to Transonic Wing Optimization
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Multiobjective evolutionary computation for supersonic wing-shapeoptimization
IEEE Transactions on Evolutionary Computation
Introduction to Evolutionary Multiobjective Optimization
Multiobjective Optimization
Artificial Intelligence Review
Advances in evolutionary multi-objective optimization
SSBSE'12 Proceedings of the 4th international conference on Search Based Software Engineering
Asynchronous master-slave parallelization of differential evolution for multi-objective optimization
Evolutionary Computation
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This paper describes an application of Adaptive Range Multiobjective Genetic Algorithms (ARMOGAs) to aerodynamic wing optimization. The objectives are to minimize transonic and supersonic drag coefficients, as well as the bending and twisting moments of the wings for the supersonic airplane. A total of 72 design variables are categorized to describe the wing's planform, thickness distribution, and warp shape. ARMOGAs are an extension of MOGAs with the range adaptation. Four-objective optimization was successfully performed. Pareto solutions are compared with Pareto optimal wings obtained by the previous three-objective optimization and a wing designed by National Aerospace Laboratory (NAL).