Industrial Application of Multi-agent Systems

This paper delves into the industrial applications of multi-agent systems (MAS), emphasizing their transformative role across diverse disciplines of science and technology. MAS has revolutionized industries by introducing decentralized, intelligent decision-making in robotics, cybersecurity, smart cities, biomedical engineering, and electric vehicles (EVs). The paper emphasizes the construction of biological networks using Probability Collective MAS (PCMAS) for drug response predictions and the integration of multi-agent deep reinforcement learning (MADRL) in grid-level optimization to enhance cost efficiency and energy management. Challenges such as communication disruptions in cyber physical systems (CPS) are addressed with sliding mode controllers, while hypergraph theory demonstrates its utility in comprehending complex relationships in genetics and psychology. Applications in Unmanned Aerial Vehicles (UAVs) exhibit improved resource allocation and coordination via multi-agent frameworks. Finally, we investigate MAS-driven distributed control for stabilizing DC bus voltage in microgrids, optimizing renewable energy integration, and managing EV charging. This work underscores MAS’s adaptability and reliability in addressing industrial challenges.