Deploying Ammunition with UAV Swarms Based on Multistrategy Public Goods Game

In modern military operations, distributed autonomous collaboration is critical for enabling unmanned aerial vehicle (UAV) swarms to balance individual platform constraints and collective mission objectives during ammunition deliveries. The existing strategies often exhibit suboptimal cooperation, limiting their performance in dynamic adversarial scenarios. To address this issue, a multistrategy public goods game (MPGG) model-based method that integrates evolutionary game theory with swarm coordination dynamics is proposed to model the collaborative resource allocation process. Tactical requirements are mapped to establish a framework with adaptive payoff functions and strategy update rules that reflect real-world military constraints. A refined aspiration-driven dynamics mechanism with variable incentives promotes cooperative behaviors. By using multiagent modeling and simulation techniques, how parameters such as the payoff coefficient and aspiration level influence swarm cooperation and strategy distribution effects is analyzed in this study. The cooperation levels under different selection strengths significantly improve as the number of strategies increases; The greater the selection strength is, the more obvious the improvement effect achieved under the same number of strategies, and the earlier it tends to approach 100%. The results demonstrate that the multistrategy evolutionary approach significantly enhances the degree of cooperation within the swarm. The proposed approach demonstrates the potential for evolutionary game theory to drive breakthroughs in swarm intelligence, offering actionable guidance for real-world tactical applications.