Dynamic decision-making of UAV swarm based on constrained multi-objective optimization under incomplete interference information

The decision-making and resource allocation of UAV swarms play a crucial role in dynamic, uncertain environments. In such complex scenarios, UAV swarms need to effectively collaborate and communicate in frequently changing interference conditions. However, existing resource allocation methods typically assume complete interference information or are suitable only for static environments, leading to significant performance degradation in the face of external uncertainties and incomplete information. To address these challenges, this paper employs fuzzy set theory to dynamically model the uncertainty of external interference and defuzzify its impact on the available frequency bands during iterative diagnostics. Additionally, a dynamic constrained multi-objective optimization model is developed, and a novel Dynamic Constrained Multi-Objective Evolutionary Algorithm based on Transfer Search (TrS-DCMOEA) is proposed. By integrating transfer learning and dynamic adjustment strategies, the algorithm quickly adapts to environmental changes, ensuring communication performance while maintaining the security of UAV swarm communications. Simulation results show that the proposed algorithm achieves superior decision-making and resource allocation efficiency in most time slots, with TrS-DCMOEA particularly excelling in tracking the Pareto front in dynamic environments.