Ricerc@Sapienza

Multi-agent systems recently gained a lot of attention for solving machine learning and data mining problems. Furthermore, their peculiar divide-and-conquer approach is appealing when large datasets have to be analyzed. In this paper, we propose a multi-agent classification system able to tackle large datasets where each agent independently explores a random small portion of the overall dataset, searching for meaningful clusters in proper subspaces where they are well-formed (i.e., compact and populated).

Bio-inspired algorithms like Genetic Algorithms and Fuzzy Inference Systems (FIS) are nowadays widely adopted as hybrid techniques in improving goods and services. In this paper we present an interesting application of the fuzzy-GA paradigm to the problem of energy flows management in microgrids, concerning the design, through a data driven synthesis procedure, of an Energy Management System (EMS).

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.