A triple-adaptive improved ant colony optimization algorithm
TL;DR
The improved algorithm generates smoother paths, with the optimal fitness improved by 15.54% and the algorithm runtime efficiency improved by 24.15%, demonstrating better comprehensive performance in both path quality and computational speed.
Abstract
3D path planning is a key technology in fields such as UAV navigation and intelligent inspection, and its planning performance directly impacts mission effectiveness. Traditional ant colony algorithms adopt fixed parameters, which frequently give rise to problems including slow convergence speed, a tendency to fall into local optimal solutions, and in sufficient global search capability in complex 3D environments. To overcome these limitations, this paper presents a triple-adaptive improved ant colony optimization algorithm. By adaptively adjusting the pheromone factor α and the heuristic factor β via a logarithmic function, and by designing a constrained adaptive pheromone evaporation coefficient ρ , the algorithm enhances both global search and local optimization capabilities. Experimental results demonstrate that, compared to the traditional algorithm, the improved algorithm generates smoother paths, with the optimal fitness improved by 15.54% and the algorithm runtime efficiency improved by 24.15%, demonstrating better comprehensive performance in both path quality and computational speed. This effectively overcomes the original deficiencies and meets the requirements of path planning in complex 3D environments.