An Improved Multi-Objective GA for Low Frequency Metamaterial Unit Robust Optimization under Uncertainty

Metamaterial (MM) is very promising in engineering application since it exhibits extraordinary physical properties that do not exist in nature. Nevertheless, the development of a MM still faces bottleneck problems such as to maximize negative permeability and ensure the robustness of the high permeability at the working frequency in engineering applications. To address the inefficiencies of existing multi-objective robust optimization methodologies in applications to MM designs, an improved multi-objective genetic algorithm and an adaptive response surface model are proposed. The numerical optimization results of a prototype MM unit have demonstrated the feasibility and merits of the proposed methodology.