Evaluation and Evolution Method of Command and Control Structures in UAV Swarm Operations Based on Potential Energy

Based on the analysis of factors influencing the combat mission process, considering heterogeneous nodes, constraint relationships, and various influencing factors, this study proposes a command and control structure evaluation method based on the concept of potential energy in the military operations domain for unmanned swarm combat. The adaptive evolution of the command and control structure for unmanned swarms under dynamic tasks is investigated, where the potential energy reward for stage tasks and the structural adjustment cost constitute the evolution benefits. A mathematical model for the adaptive evolution of the command and control structure is constructed with the objective of maximizing the evolution sequence benefits. An algorithm based on reinforcement learning (PPO) and genetic algorithms (GA) is designed to solve the command and control structure evolution sequence under dynamic tasks. GA is used to search for command and control structures adapted to different stage tasks, while PPO is used to search for the evolution path under stage task transitions.