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.

To enhance combat effectiveness and address the challenges of the coordinated scheduling of naval combat equipment, this paper proposes a graph-model-based adaptive maritime kill web generation method.

The proposed method encompasses four key parts. Through battlefield situation modeling, the real-time access and integration of multi-source information are carried out to construct a dynamic battlefield model. A complex task decomposition module is utilized to break down combat tasks into executable subtasks and optimize resource allocation. The kill web is generated based on equipment relationships and capability indicators, and optimized under multiple objective constraints. When the equipment resources change, the kill web is adaptively reconstructed through redundant node supplementation and other means.

Verified by a maritime anti-missile scenario experiment, this method effectively solves the problems of battlefield situation information processing, task decomposition and modeling, and equipment collaborative optimization and dynamic adjustment, and can quickly generate and optimize a kill web to achieve multi-chain and multi-angle defense.

The proposed graph model-based maritime adaptive kill web generation method can improve the overall effectiveness and response ability of modern naval warfare. Future research will continue to optimize the algorithm performance and system response ability to provide stronger support for military operations.