Multi-agent system

About: Multi-agent system is a research topic. Over the lifetime, 27978 publications have been published within this topic receiving 465191 citations. The topic is also known as: multi-agent systems & multiagent system.

Self-organizing network services with evolutionary adaptation

Abstract: This paper proposes a novel framework for developing adaptive and scalable network services. In the proposed framework, a network service is implemented as a group of autonomous agents that interact in the network environment. Agents in the proposed framework are autonomous and capable of simple behaviors (e.g., replication, migration, and death). In this paper, an evolutionary adaptation mechanism is designed using genetic algorithms (GAs) for agents to evolve their behaviors and improve their fitness values (e.g., response time to a service request) to the environment. The proposed framework is evaluated through simulations, and the simulation results demonstrate the ability of autonomous agents to adapt to the network environment. The proposed framework may be suitable for disseminating network services in dynamic and large-scale networks where a large number of data and services need to be replicated, moved, and deleted in a decentralized manner.

Multi-agent systems for grid energy management: A short review

Abstract: The electric grid is currently undergoing important changes: it is evolving from an entirely centralized structure to a decentralized one, mainly due to the massive development of distributed renewable energy sources. This evolution toward what we now call the smart grid requires new control methods. These methods must be able to withstand new requirements, such as the highly distributed nature of the grid, the ability to run in islanding mode, the intermittency of renewable energy sources and the limited bandwidth for communications. Multi-agent systems (MAS) have characteristics that meet these requirements. In contrary to classical analytical methods, the grid is considered as a collection of simple entities called agents corresponding to sources, loads and other components, evolving in a given environment. A certain degree of distributed or collective intelligence can be achieved through the interaction of these agents with each other, cooperating or competing to reach their goals. This article is a short review of several multi-agent systems published in the literature and used for grid energy management. It presents a number of concepts and experiments used by researchers to apply this promising method. Various approaches and their results are compared, in order to give the reader a global perspective of the state of the art in this precise domain.

Event-Triggered Communication and Data Rate Constraint for Distributed Optimization of Multiagent Systems

Abstract: This paper is concerned with solving a large category of convex optimization problems using a group of agents, each only being accessible to its individual convex cost function. The optimization problems are modeled as minimizing the sum of all the agents’ cost functions. The communication process between agents is described by a sequence of time-varying yet balanced directed graphs which are assumed to be uniformly strongly connected. Taking into account the fact that the communication channel bandwidth is limited, for each agent we introduce a vector-valued quantizer with finite quantization levels to preprocess the information to be exchanged. We exploit an event-triggered broadcasting technique to guide information exchange, further reducing the communication cost of the network. By jointly designing the dynamic event-triggered encoding–decoding schemes and the event-triggered sampling rules (to analytically determine the sampling time instant sequence for each agent), a distributed subgradient descent algorithm with constrained information exchange is proposed. By selecting the appropriate quantization levels, all the agents’ states asymptotically converge to a consensus value which is also the optimal solution to the optimization problem, without committing saturation of all the quantizers. We find that one bit of information exchange across each connected channel can guarantee that the optimiztion problem can be exactly solved. Theoretical analysis shows that the event-triggered subgradient descent algorithm with constrained data rate of networks converges at the rate of ${O}( {\ln t/{\sqrt {t}}})$ . We supply a numerical simulation experiment to demonstrate the effectiveness of the proposed algorithm and to validate the correctness of theoretical results.