Clustering Allocation for Large-Scale Multi-Agent Systems: A Coalitional Game Method

Motivated by the inefficiencies where multi-agent systems fail to reconcile individual agent self-interest with global optimality and accommodate dynamic tasks as its population increases, this paper investigates a clustering allocation problem for large-scale multi-agent systems. A novel coalitional game clustering allocation scheme that can simultaneously reconcile individual agent self-interest and adapt to dynamic tasks is proposed. In this scheme, a coalition switching strategy is newly constructed and incorporated to select optimal switching operation and obtain stable coalition partition. Simulation and comparative results are provided to verify the effectiveness of the developed allocation scheme. It is shown both theoretically and simulation experimentally that in the case of large-scale multi-agent systems, the generated clustering allocation strategy is Nash stable using the proposed scheme.