TL;DR
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.
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.
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.
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.
The Personal History Memory Mechanism is introduced, which replaces random perturbation with weighted historical experience to enhance the directional search capability of the algorithm, and a greedy selection strategy is embedded to ensure the monotonically non-deteriorating quality of population solutions.
A reinforcement learning-based multi-strategy differential evolution algorithm, named PPOMSDE, which generates safer and smoother flight paths while maintaining accurate terrain-following flight, reducing the overall cost by an average of 22.42% compared with ISDE, L-SHADE, SHADE, and ISHACDE.
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...
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