In order to improve our military's level of intelligent accusation decision-making in future intelligent joint warfare, this paper studies operation loop recommendation methods for kill web based on the fundamental combat form of the future, i.e., “web-based kill,” and the operation loop theory. Firstly, we pioneer the operation loop recommendation problem with operation ring quality as the objective and closed-loop time as the constraint, and construct the corresponding planning model. Secondly, considering the case where there are multiple decision objectives for the combat ring recommendation problem, we propose for the first time a multi-objective optimization algorithm, the multi-objective ant colony evolutionary algorithm based on decomposition (MOACEA/D), which integrates the multi-objective evolutionary algorithm based on decomposition (MOEA/D) with the ant colony algorithm. The MOACEA/D can converge the optimal solutions of multiple single objectives non-dominated solution set for the multi-objective problem. Finally, compared with other classical multi-objective optimization algorithms, the MOACEA/D is superior to other algorithms superior in terms of the hyper volume (HV), which verifies the effectiveness of the method and greatly improves the quality and efficiency of commanders' decision-making.

Today, people rely heavily on infrastructure networks. Attacks on infrastructure networks can lead to significant property damage and production stagnation. The game theory provides a suitable theoretical framework for solving the problem of infrastructure protection. Existing models consider only the beneficial effects that the defender obtains from information gaps. If the attacker’s countermeasures are ignored, the defender will become passive. Herein, we consider that a proficient attacker with a probability in the game can fill information gaps in the network. First, we introduce the link-hiding rule and the information dilemma. Second, based on the Bayesian static game model, we establish an attack–defense game model with multiple types of attackers. In the game model, we consider resource-consistent and different types of distributions of the attacker. Then, we introduce the solution method of our model by combining the Harsanyi transformation and the bi-matrix game. Finally, we conduct experiments using a scale-free network. The result shows that the defender can be benefited by hiding some links when facing a normal attacker or by estimating the distribution of the attacker correctly. The defender will experience a loss if it ignores the proficient attacker or misestimates the distribution.

Unmanned aerial vehicles (UAVs) play an increasingly vital role in maritime search and rescue (SAR) because they can be deployed quickly, cover large ocean areas, and operate without exposing human crews to risk. Compared with single platforms, multi-UAV cooperation improves efficiency in locating drifting targets influenced by wind and currents. However, existing allocation methods often focus only on immediate task benefits and neglect search history, leading to redundant revisits and lower overall efficiency. To address this problem, we propose a hybrid auction–pheromone framework for multi-UAV maritime SAR. The method combines an auction-based allocation strategy, which assigns tasks according to target probability, distance, and UAV workload, with a pheromone-guided mechanism that records visitation history through exponential decay to discourage repeated searches. A layered model is constructed, consisting of an airspace/weather constraint layer, a target probability layer, a pheromone layer, and a UAV motion layer. UAVs adopt A* path planning with a nearest-first policy, while a stagnation detector triggers dynamic reallocation when coverage slows. Simulation experiments verify the effectiveness of the proposed approach. Compared with auction-only and pheromone-only baselines, the hybrid method reduces the required steps by up to 27.1%, decreases the overlap ratio to 0.135–0.164, and increases the coverage speed by 64.7%. These results demonstrate that integrating explicit auctions with implicit pheromone memory significantly enhances scalability, robustness, and efficiency in multi-UAV maritime SAR. Future research will focus on dynamic drift modeling, real-world deployment, and heterogeneous UAV collaboration.

Emergency material scheduling (EMS) is a core component of post-disaster emergency response, with its efficiency directly impacting rescue effectiveness and the satisfaction of affected populations. However, due to severe road damage, limited availability of resources, and logistical challenges after disasters, current EMS practices often suffer from uneven resource distribution. To address these issues, this paper proposes a priority-based, multi-stage EMS approach with drone coordination. First, we construct a three-level EMS network “storage warehouses–transit centers–disaster areas” by integrating the advantages of large-scale transportation via trains and the flexible delivery capabilities of drones. Second, considering multiple constraints, such as the priority level of disaster areas, drone flight range, transport capacity, and inventory capacities at each node, we formulate a bilevel mixed-integer nonlinear programming model. Third, given the NP-hard nature of the problem, we design a hybrid algorithm—the Tabu Genetic Algorithm combined with Branch and Bound (TGA-BB), which integrates the global search capability of genetic algorithms, the precise solution mechanism of branch and bound, and the local search avoidance features of Tabu search. A stage-adjustment operator is also introduced to better adapt the algorithm to multi-stage scheduling requirements. Finally, we designed eight instances of varying scales to systematically evaluate the performance of the stage-adjustment operator and the Tabu search mechanism within TGA-BB. Comparative experiments were conducted against several traditional heuristic algorithms. The experimental results show that TGA-BB outperformed the other algorithms across all eight test cases, in terms of both average response time and average runtime. Specifically, in Instance 7, TGA-BB reduced the average response time by approximately 52.37% compared to TGA-Particle Swarm Optimization (TGA-PSO), and in Instance 2, it shortened the average runtime by about 97.95% compared to TGA-Simulated Annealing (TGA-SA).These results fully validate the superior solution accuracy and computational efficiency of TGA-BB in drone-coordinated, multi-stage EMS.

The rapid development of military technology has prompted different types of equipment to break the limits of operational domains and emerged through complex interactions to form a vast combat system of systems (CSoS), which can be abstracted as a heterogeneous combat network (HCN). It is of great military significance to study the disintegration strategy of combat networks to achieve the breakdown of the enemy's CSoS. To this end, this paper proposes an integrated framework called HCN disintegration based on double deep $Q$-learning (HCN-DDQL). Firstly, the enemy's CSoS is abstracted as an HCN, and an evaluation index based on the capability and attack costs of nodes is proposed. Meanwhile, a mathematical optimization model for HCN disintegration is established. Secondly, the learning environment and double deep $Q$-network model of HCN-DDQL are established to train the HCN's disintegration strategy. Then, based on the learned HCN-DDQL model, an algorithm for calculating the HCN's optimal disintegration strategy under different states is proposed. Finally, a case study is used to demonstrate the reliability and effectiveness of HCN-DDQL, and the results demonstrate that HCN-DDQL can disintegrate HCNs more effectively than baseline methods.