Multi-objective optimization for aerodynamic designs by using ARMOGAs

Global trade-offs for aerodynamic design of Supersonic Transport (SST) have been investigated by Multi-Objective Evolutionary Algorithms (MOEAs). The objectives are to reduce both drag and sonic boom to make next-generation SST more feasible. Adaptive Range Multi-Objective 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.