Task Assignment Model of Hypersonic Vehicle Swarm Based on Path-Aware Stackelberg-GA Game Model

This paper proposes a path-aware Stackelberg game model for Hypersonic Vehicle Swarm task assignment in complex battlefield environments featuring no-fly zones. The core innovation lies in establishing a quantitative reaction function between path characteristics and interception probability to model realistic adversarial dynamics. An integrated computational framework combining A∗ path planning and genetic algorithm optimization is developed to solve the hierarchical assignment problem, where the attacker (leader) maximizes net expected utility while anticipating path-dependent defense reactions. The framework accounts for operational constraints including nofly zones and missile exclusivity. Validated through multi-scale simulations, the approach generates tactically coherent assignments that balance target value, path cost, and interception risk. Crucially, the model reveals that the swarm may rationally choose a longer path if it results in a disproportionately lower defensive reaction, thereby maximizing the final destruction probability and mission utility.