RESUMO
We propose an efficient and versatile optimization scheme, based on the combination of multi-objective genetic algorithms and neural-networks, to reproduce specific colors through the optimization of the geometrical parameters of metal-dielectric diffraction gratings. To illustrate and assess the performance of this approach, we tailor the chromatic response of a structure composed of three adjacent hybrid V-groove diffraction gratings. To be close to the experimental situation, we include the feasibility constraints imposed by the fabrication process. The strength of our approach lies in the possibility to simultaneously optimize different contradictory objectives, avoiding time-consuming electromagnetic calculations.
RESUMO
We propose a metamodel-based optimization technique to tailor the chromatic response of high-contrast-index gratings. The algorithm, which couples a population-based metaheuristic with a neural network, is used to retrieve the optimal geometrical parameters of a grating to reproduce a prescribed color. By means of some examples, we assess the possibilities and limitations of our optimization scheme. The numerical evidence found shows that the metamodel approach offers an alternative to traditional metaheuristic techniques that not only provides the best solution for a given geometry and a material but also significantly improves the computing time required for the optimization process.
