Surrogate based MOEA/D for electric motor design with scarce function evaluations
This paper proposes a novel surrogate assisted multi objective evolutionary algorithm based on decomposition (s MOEA/D) for the design of electric motors. The idea is to improve the surrogate gradually during the optimization. Simulation results show that the proposed method can outperform a regular multi objective optimization algorithm with only a small portion of the computational cost.
2016 IEEE Conference on Electromagnetic Field Computation (CEFC)