A three-role dynamic multi-swarm crow search algorithm (3R-DMCSA) is proposed, in which exploiter, explorer, and diversifier role-based swarms share a crow search-based update structure, feasibility-aware comparison, and leader-selection structure but use TA- and PP-specific encodings, objective preferences, initialization, and dynamic responses.
Abstract
Dynamic events require coordinated task allocation (TA) and path planning (PP) for multiple unmanned aerial vehicles (UAVs) to maintain executable mission progress. Existing coupled methods often use path information only as a precomputed cost or downstream refinement result, limiting its reuse after dynamic changes. This paper formulates dynamic task allocation and path planning (DTAPP) as a dynamic multi-objective optimization problem considering remaining target value, mission makespan, path feasibility, and execution-state inheritance. A three-role dynamic multi-swarm crow search algorithm (3R-DMCSA) is proposed, in which exploiter, explorer, and diversifier role-based swarms share a crow search-based update structure, feasibility-aware comparison, and leader-selection structure but use TA- and PP-specific encodings, objective preferences, initialization, and dynamic responses. A path matrix connects the layers by storing candidate paths and their attributes, which are fed back to TA, and supporting rolling-horizon leading flight-segment refinement. Experiments involving three dynamic urban scenarios compare the method with five baselines and evaluate its path-matrix feedback and rolling-horizon refinement. Compared with the strongest baseline, our approach improves mission-value acquisition by 10.6%, 16.2%, and 32.0% in the three scenarios, while maintaining near-complete target coverage and reliable flight-segment execution. Path-matrix feedback improves mission-value acquisition by 5.2–26.1% over the configuration without PP-to-TA path feedback, while rolling-horizon segment refinement reduces replanning latency by 48.8–70.5% compared with refining all planned segments without significantly compromising mission performance.
3D UAV trajectory planning in dynamic multi-threat environments as a dynamic bi-objective optimization problem and a multi-swarm dynamic multi-objective crow search algorithm (MDMCSA) are formed.
Gengsong Li, Yi Liu, Qibin Zheng et al.· Applied Sciences· 0 citations
To address the challenges of collaborative task allocation and path planning for multiple logistics unmanned aerial vehicles (UAVs) in urban low-altitude environments, this paper proposes a bilevel nested joint optimization method based on reinforcement learning and a graph search algorithm to enhance the efficiency of collaborative last-mile delivery by multiple logistics UAVs while reducing flight risks. The proposed method constructs a bilevel architecture system based on a task allocation and decision-making model and a path planning model. The upper-level model holistically considers the demands of three stakeholders—government (safety), customers (timeliness), and UAV enterprises (economy)—at the macro level. Then, based on real-time order information and UAV status, a multi-objective optimization and constraint model is constructed under complex dynamic environments. A multi-agent proximal policy optimization algorithm is employed to achieve rapid dynamic task allocation and decision-making. The lower-layer model utilizes the upper-level allocation results combined with detailed environmental information to plan safe and efficient flight paths for each UAV at the micro level. It employs an improved jumping-point search algorithm for refined path optimization. A loop feedback mechanism is designed to facilitate information exchange between layers, thereby coupling the task allocation and path planning processes to achieve collaborative optimization of upper- and lower-level task allocation and decision-making. This method effectively addresses complex logistics delivery scenarios, enhancing the overall efficiency and robustness of the delivery system. Simulation experiments comprehensively consider path influences from flexible open-area delivery, varying numbers of distribution centers and UAVs, and on-demand rush orders. Tests conducted in medium- and high-density environments demonstrate the proposed model and algorithm’s significant superiority in dynamic complex scenarios. Even when confronted with complex environments and dynamic order scenarios, it consistently generates highly applicable UAV flight paths.
Zongwei Li, Guang Zhang, Heyun Gao· Journal of Vibration and Con...· 0 citations
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.
M. Nikolaiev, M. Novotarskyi· International Journal of Wir...· 0 citations
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).
Wenguang Yang, Yi-Kang Du, Lianhai Lin· Memetic Computing· 0 citations
A hierarchical joint optimization algorithm is developed within a multi-agent deep reinforcement learning (MADRL) framework to coordinate UAVs and MTs in a distributed manner and outperforms other benchmarks under varying network scales and capabilities by jointly optimizing UAV operations and resource utilization.
Tiankui Zhang, Wenlong Xu, Tianyi Shi et al.· IEEE Internet of Things Jour...· 0 citations
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.
Yalan Peng, Haibin Duan, Ming Li et al.· Science China Technological...· 0 citations
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