Ricerc@Sapienza

The World energy demand is progressively growing, so that many different Renewable Energy Sources (RESs) are exploited to meet the user needs and to reduce the Global Warming. In this context, an emerging RES is the sea wave energy, because it can offer a better continuity in the energy production by taking advantage of the stationary nature of the waves. Research in the energy harvesting from waves has led to the development of Wave Energy Converters (WECs). The adoption of Computational Intelligence techniques become crucial for maximizing the WECs efficiency. Therefore, the optimization of the energy transduction of a specific WEC model has been investigated in this paper. More precisely, three different Evolutionary Algorithms (EAs), namely a Genetic Algorithm (GA), a Particle Swarm optimization (PSO) and a Hybrid Genetic PSO (HG-PSO), have been considered to determine the optimal values of the main WEC parameters. Both the effectiveness and the efficiency of the above algorithms have been tested aiming at finding which of them is most suitable for the considered problem. The obtained results showed promising performances, with all the three algorithms achieving effective and robust solutions. In particular, the HG-PSO proved to be the most suitable approach, being the most effective and efficient algorithm even in front of a more rigid stop condition.