Optimal selection strategy of surface-to-air anti-missile kill chain based on mixed swarm evolutionary meta-game

To optimize the kill chain design process and enhance combat capabilities, this study investigates a kill chain optimization algorithm based on a hybrid swarm evolutionary meta-game.

Focusing on surface-to-air defense, a non-cooperative game model is developed to address decision-making challenges within kill chain optimization. The game involves UAVs, USVs, and the interplay between damage probability, weapon cost, and remaining USV capability. For UAVs, the game considers target illumination time and remaining UAV capability. A Nash equilibrium-based algorithm is proposed to solve these game models. Given the exponential growth in feasible solutions as the number of targets, sensing nodes, and strike nodes increases, the study introduces an evolutionary meta-game algorithm using real-number encoding to solve the problem efficiently.

The simulation results show that in the uniform attack mode, the optimal Nash equilibrium value decreases monotonically with iterations, effectively yielding optimal kill chain solutions for 8, 16, and 32 incoming targets. Compared to other algorithms, the proposed method outperforms in all metrics, validating its effectiveness.

The proposed hybrid swarm evolutionary meta-game algorithm effectively integrates multi-node resources in maritime operations and dynamically adjusts the allocation of sensing and strike nodes to achieve the rapid closure of the kill chain and optimal strategies. Future research can expand the scenarios and refine the model to include more missile types, complex attack patterns, and resource allocation priorities for different defense systems, further validating the algorithm's performance.