Multi-objective optimization for aerodynamic designs by using ARMOGAs

Global trade-offs for aerodynamic design of supersonic transport (SST) have been investigated by multiobjective evolutionary algorithms (MOEAs). The objectives are to reduce both drag and sonic boom to make next-generation SST more feasible. Adaptive range multiobjective genetic algorithms (ARMOGAs) are utilized for the efficient search. The trade-offs are analysed by self-organizing map (SOM), which provides a topology preserving mapping from the high dimensional space to two dimensions. ARMOGAs and SOM can be good design tools to conduct aerodynamic design optimizations and analyse the results.