Multi-objective evolutionary algorithm as a method to obtain optimized nanostructures

The field of plasmonics, an optics discipline that studies the interaction of light with matter for structures with dimensions similar to the wavelength of the electromagnetic radiation affecting them, has been further developed with the support of computational technologies that are capable of performing calculations with large volumes of data to solve the complex problems of this discipline. Some of the problems in plasmonics require the use of algorithmic techniques that can simultaneously handle more than one function that tend not to present their maximum or minimum at the same point, i.e., their optimal performances conflict with each other. In this paper, we present the results of the use of a multi-objective genetic algorithm to obtain the maximum plasmonic resonance in nanoparticles assuming three relevant factors: geometry, current density, and electric field, which are, in turn, the three objective functions for the proposed algorithm. The method used for the characterization of the nanoparticles was a numerical simulation using the finite element method. To verify the results, the electromagnetic radiation patterns and other optical properties of the obtained nanoparticles were compared with those of nanoparticles reported in the literature. Possible applications and work in progress are also discussed.