Novel Memetic Algorithm implemented With GA (Genetic Algorithm) and MADS (Mesh Adaptive Direct Search) for Optimal Design of Electromagnetic System

This paper presents the novel implementation of the memetic algorithm with GA(Genetic Algorithm) and MADS(Mesh Adaptive Direct Search), which is applied for the optimal design methodology of the electric machine. This hybrid algorithm has been developed for obtaining the global optimum rapidly, which is effective for the optimal design of a electric machine with many local optima and much longer computation time. As a meta-heuristic search algorithm, MADS combined with a GA is validated with the Rastrigin function and the Shubert function with distinguished multimodal characteristics by investigating the evaluation number for optima convergence. In particular, the proposed algorithm has been forwarded to the optimal design of a direct-driven PM wind generator for maximizing the Annual Energy Production(AEP), of which design objective should be obtained by FEA(Finite Element Analysis). Finally, it is shown that MADS combined with GA has contributed to reducing the computation time effectively for the optimal design of a PM wind generator when compared with the purposely developed GA implemented with the parallel computing method.