Particle Swarm Optimization of Resonant Sonic Crystals Noise Barriers

The study of technological materials made by meticulously arranging acoustic elements has received a lot of attention over the past three decades with the goal of generating improved acoustic properties, often going beyond the behavior of materials found in nature. These are frequently referred to as acoustic metamaterials, and because of the way wave propagation phenomena is managed, they exhibit unusual properties. Improvements in noise mitigation techniques of acoustic systems based on metamaterials principles have been made effectively and precisely using combined or hybrid numerical methods and improved numerical formulations. These noise mitigation properties should be optimized by modifying topology and inner elements properties, which requires a huge search space. This work focuses on metamaterials called sonic crystals with Helmholtz resonators, and its innovative insulation properties as noise barriers are optimized with the Particle Swarm Optimization (PSO). This evolutionary algorithm provides a mono objective solution for the multi-dimensional search space inspired in animal behavior looking for food and communicating between each other. In order to assess the results obtained by the proposed approach, the presented PSO algorithm is compared with a Genetic Algorithm (GA): the results show that the PSO algorithm provides a better solution that pursues the objective of satisfying the acoustic comfort without exceeding the imposed practical constraints.