Simultaneous arrival path planning for multi-UAVs with communication maintenance constraints

To address the cooperative path planning problem of multi-UAV systems under multiple constraints, a constrained multi-objective optimization algorithm based on dual-population cooperative evolution (DPC-MOEA) is proposed. This algorithm comprehensively considers multiple constraints including path feasibility, temporal synchronization, and communication connectivity, aiming to enhance the overall performance and mission reliability of multi-UAV collaborative operations. First, a multi-objective optimization model for cooperative multi-UAV path planning is established. A time-window-based coordination constraint mechanism is introduced, together with communication maintenance constraints derived from LoS and NLoS propagation models, enabling coordinated handling of temporal synchronization and communication connectivity. Subsequently, a dual-population cooperative co-evolution framework is designed. By integrating strategies such as constraint-specific adaptive penalties, region-adaptive weighting, and constraint-complementary pairing, the framework balances convergence and diversity, thereby improving the efficiency of feasible solution search. Finally, Finally, the effectiveness of the proposed algorithm is validated through multi-scenario comparisons involving different numbers of UAVs. Results demonstrate that DPC-MOEA can achieve efficient, safe, and stable cooperative path planning for multi-UAV systems under constrained environments.