Research on Hiking Path Planning Based on Improved Ant Colony Algorithm

To address the issues of early blind search, slow convergence speed, and susceptibility to local optima in traditional Ant Colony Optimization (ACO) algorithms for three-dimensional mountain path planning, an improved ACO algorithm was proposed. This algorithm was integrated with the dynamic step length attenuation mechanism of the Beetle Antennae Search (BAS) algorithm and an exponential step length attenuation factor was introduced. Initially, a broad global search was conducted to avoid local optima, followed by a refined local search to improve search accuracy. Additionally, a weighted average smoothing method was employed to smooth the path, preventing discontinuities or abrupt changes. It is demonstrated through simulation experiments that, compared to traditional algorithms, the proposed algorithm reduces the average shortest path length by 20.6% and 22.9%, respectively. The average running time is reduced by 89.7% and 89.0%, respectively, and the average number of turns is reduced by 52.9% and 44.4%. This algorithm is suitable for hiking path planning, reducing planning time and improving path quality, thus providing technical support for optimizing hiking paths in three-dimensional mountainous environments without existing road networks.