TL;DR
The Circle-SPM chaotic map is introduced to optimize the population initialization process, effectively mitigating the premature convergence caused by uneven distribution and a lack of population diversity.
Experimental results show that the proposed improved dung beetle optimization (IDBO) algorithm, which integrates multiple coordinated mechanisms to enhance the original dung beetle optimizer, can serve as a competitive optimizer for numerical benchmark problems and offline static 3D UAV path-planning simulations.
Comparative experiments demonstrate that MsESO exhibits higher robustness and superiority over CMA-ES, MadDE, LSHADE-SPACMA, WOA, HHO, PPSO, MELGWO, HLOA, NRBO, ESO, and the original SO, and outperforms the comparative algorithms in UAV path planning problems, showcasing its significant potential in practical applicati...
Simulation experiments show that in a complex obstacle environment of 100m × 100m× 100m, the IAGA algorithm can effectively plan the optimal path, and the convergence speed is significantly improved compared to traditional genetic algorithms, verifying the effectiveness and robustness of this method in handling multi-c...
An efficient and robust algorithm for autonomous three-dimensional path planning of UAVs in complex obstacle environments is presented and coordinated improvements realize targeted optimization for UAV 3D flight characteristics.
Experiments show that HLGWO generally outperforms several comparison algorithms in convergence accuracy, stability, and path cost, thereby improving the safety, feasibility, and optimization performance of 3D UAV path planning in complex environments.
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.
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