Swarm Maneuver Decision Method Based on Learning-Aided Evolutionary Pigeon-Inspired Optimization for UAV Swarm Air Combat

Unmanned aerial vehicle (UAV) swarm dynamic combat poses significant challenges due to its complexity and dynamism. This study introduces a novel approach that addresses these challenges through the development of a swarm maneuver decision method based on the Learning-Aided Evolutionary Pigeon-Inspired Optimization (LAEPIO) algorithm. This research proceeds systematically as follows: First, a nonlinear model of fixed-wing UAVs and a decision-making system for swarm air combat are established. Next, a situation function is applied to characterize the battlefield environment and quantify the strategic advantages of each side during the engagement. The LAEPIO algorithm is then advanced to tackle sub-tasks in swarm air combat by incorporating a learning-aided evolutionary mechanism. Building upon this foundation, a swarm maneuver decision method is designed, enabling UAV swarms to select optimal strategies from a library of maneuvers after thoroughly assessing the battlefield scenario. Finally, the efficacy and superiority of the proposed method are demonstrated through comprehensive simulations across diverse air combat scenarios. The results show that the average win rate of the proposed algorithm is 36.7% higher than that of similar algorithms.