Many-objective optimization approaches for the multi-physics electric motor design problem

Designing electric traction motors involves simultaneously optimizing multiple objectives across electromagnetic, thermal, and mechanical domains. As the number of objectives grows, Pareto-based methods become less effective due to the increase in non-dominated solutions. This work investigates how the number and distribution of non-dominated points vary with the number of objectives, using a dataset of 16000 motor configurations. Results show that many-objective settings (more than four) pose challenges for Pareto ranking. Building on these insights, many-objective optimization techniques such as NSGA-III and game-theory-based aggregation will be investigated and compared, using surrogate models trained on this dataset to reduce the computational effort.