Research on Task Collaboration Over Heterogeneous Networks Based on Evolutionary Game Theory

With the increasing level of machine intelligence, the problem of collaboration among intelligent individuals in heterogeneous networks has become a focal point of research. Taking the task allocation of heterogeneous Unmanned Aerial Vehicle (UAV) swarms as an example, we study the game behavior of heterogeneous network based on task traction. To align with the autonomous and collaborative decision-making process of unmanned swarms, we construct a multi-party multi-strategy evolutionary game framework on heterogeneous networks, define local and global game payoff functions, and innovatively propose an Enhanced Moran Rule that integrates “Pairwise updating” and “Virtual Game” (PVG-EMR algorithm) to improve the task collaboration of UAV swarm. Experiments on various underlying communication network models show that the PVG-EMR algorithm proposed in our study can effectively plan the collaborative object of UAVs and ensure the appropriate assignment of tasks in the heterogeneous network, optimizing both local and global payoffs. Moreover, the algorithm exhibits robust performance.