Research on strategies to improve convergence efficiency of multi-agent systems based on RAV algorithm

With the continuous development of wireless networks and communication technologies, multi-agent systems (MAS) are increasingly widely used in various fields. As a distributed algorithm, MAS aims to achieve consistency among all agents. However, the existence of redundant links is an obstacle to system efficiency. Redundant links require agents to process communication information from unnecessary neighbors, which reduces the convergence speed and consistency of the system. To solve this problem, this paper proposes an algorithm that combines redundant link removal with a voting mechanism (Voterank). The algorithm first constructs a local subsystem in the multi-agent system, and identifies and deletes redundant links through the mathematical characteristics of the subsystem. Experimental results show that the algorithm can operate effectively in subsystems of different sizes, and the improved voting mechanism identifies key nodes in the subsystem to avoid mistakenly deleting their connections, thereby ensuring the accuracy of the algorithm. The final results show that the algorithm can accelerate the convergence speed of consistency in the multi-agent system. In summary, the algorithm combining redundant link removal with a voting mechanism (Redundant Links And Voterank, RAV) shows certain performance in improving the efficiency and consistency of the multi- agent system.