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.
Simulation results indicate that the proposed modified version of whale optimization algorithm (MVWOA) outperforms some state-of-the-art algorithms considering on accuracy and convergence, demonstrating its potential for solving applications.
An improved Grey Wolf Optimizer (IGWO) is proposed, which effectively adapts GWO to discrete spaces and achieves optimal paths across datasets of varying scales and provides efficient UAV path planning solutions for navigation mark inspection and offer technical support for smart maritime supervision systems.
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.
With the popularization of unmanned aerial vehicles (UAVs) in scenarios such as military reconnaissance, logistics transportation, and post-disaster rescue, Generating optimal flight paths that guarantee both safety and timeliness amidst high-density barriers and unknown environmental factors presents a formidable chal...
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