In the military field, decision making has become the core of the new operational concept, known as the “kill web”. Although the theory of kill web has been widely recognized by many countries, the decision-making methods for the kill web are still in the early stage. Therefore, there is a need for a new decision-making method for the kill web. Firstly, different from the traditional scheme decision, the kill web is a complex system. The method of complex network provides a new perspective on complex systems, so the kill web was modeled based on complex network. Secondly, the kill web relies on artificial intelligence to provide decision-makers with operation loop solutions, and then decision-makers rely on the experience to make a final decision. However, the current decision-making methods only consider one of the intelligent and human decision-making methods, while the kill web needs to consider both. Hence, we combined intelligent decision making with human decision making through multi-objective optimization and the prospect theory. Finally, we designed a nondominated sorting ant colony genetic algorithm-II (NSACGA-II) to solve large-scale problems, since the kill web is a large-scale system. In addition, an illustrative case was used to verify the feasibility and effectiveness of the proposed model. The results showed that, compared with other classical multi-objective optimization algorithms, the NSACGA-II is superior to other superior algorithms in terms of the hypervolume (HV) and spacing (SP), which verifies the effectiveness of the method and greatly improves the quality of commanders’ decision-making.

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 C³I cooperative decision system is the guarantee for combat capabilities of surface warship formations. At present, research on the C3I system simulation lays more emphasis on finding structural logic defects by executing the simulation model, lacking the application of intelligent optimization algorithms to optimize parameters in the system. In this paper, the cooperative decision-making process of surface warship formation defense system is studied. Meanwhile, modelling and optimization methods for cooperative decision system are proposed. Based on simulation models built on the ExtendSim platform, this paper optimizes the staffing strategy of decision makers based on the genetic algorithm, to improve the per capita decision efficiency. The optimized staffing strategy meets objectives and requirements. The research in this paper can provide a scientific and objective reference for relevant decision-makers and researchers.