TL;DR
Simulation results show that the proposed hierarchical task planning framework significantly outperforms traditional approaches in efficiency, robustness, and scalability, highlighting its strong potential for UAV swarm mission planning in complex environments.
Trajectory planning, which determines a route from a starting position to a target position within a given airspace, is critical to unmanned aerial vehicle (UAV) mission execution. Many existing meta-heuristic approaches to three-dimensional (3D) trajectory planning aggregate competing requirements into a weighted cost and may suffer from limited adaptability when the environment changes. This paper formulates 3D UAV trajectory planning in dynamic multi-threat environments as a dynamic bi-objective optimization problem and proposes a multi-swarm dynamic multi-objective crow search algorithm (MDMCSA). The proposed method organizes objective-oriented swarms within a cooperative search framework and facilitates information exchange through archive sharing, thereby coordinating the search process among different objectives. The memory-time and diverse behavior strategies adjust search behaviors and solution perturbation to balance convergence and diversity. A hybrid change response strategy combines historical information reuse with diversity restoration after dynamic changes. Comparative experiments on dynamic benchmark problems and UAV trajectory planning scenarios demonstrate competitive convergence and adaptation performance, together with a favorable trade-off between solution quality and computational cost. Incremental ablation and parameter-sensitivity analyses further indicate the cumulative benefit of the integrated design and the stable performance of the selected parameter configuration across the tested settings.
A multi-objective intelligent optimization algorithm, the wise wayfinding algorithm (WWA), which integrates mechanisms from non-dominated sorting genetic algorithm II and multi-objective particle swarm optimization (MOPSO) and exhibits favorable convergence and robust solution distribution on standard benchmark functions (ZDT, DTLZ, UF).
Unmanned Aerial Vehicle Path Planning (UAVPP) in obstacle-rich environments requires trajectories that are collision-free, threat-aware, and feasible under practical flight constraints. This study proposes an Intelligent Cooperative Differential Evolution approach, reffered to as MuCDEA, to improve the adaptability and robustness of conventional Differential Evolution (DE) for UAVPP. MuCDEA integrates complementary mechanisms from JADE, CoDE, EPSDE, SaDE, MIDE, and SHADE through adaptive strategy selection and cooperative evolution. The optimization model combines path-length (fuel) cost and threat exposure with explicit pitch and yaw constraints that enforce actuator-feasible maneuvering bounds. The proposed framework is evaluated on 20 benchmark UAVPP cases covering 2D and 3D scenarios with varying obstacle distributions and pathh discretization levels, and it is compared against 11 state-of-the-art DE variants and several widely used optimization methods using the CEC-2022 ranking methodology. Results show that the cooperative configuration MuCDEA24 achieves the best overall ranking and consistently produces feasible trajectories across the tested cases, indicating that cooperative DE strategies provide an effective and controller-compatible solution for constrained UAVPP.
A Multi-UAV Planning Framework for Task Allocation, Route Optimization and Trajectory Smoothing for static environments with known obstacle geometry is presented and results demonstrate that the proposed hierarchical formulation is computationally effective, physically consistent, and well suited to multi-UAV mission planning.