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<p>To address the challenges associated with traditional traversal-based land leveling, which often suffered from a lack of coordination and planning, resulting in low operational efficiency and the management of large data volumes in contemporary path planning, this study proposed a variable-scale improved ant colony optimization algorithm specifically designed for land leveling path planning. The research introduced the IACO (Improved Ant Colony Optimization) algorithm for large-scale inter-region soil balance path planning, alongside the FIA*ACO (Fusion of the Improved A* Ant Colony Optimization) algorithm for small-scale, fine-grained full-field leveling path planning. Based on the analysis of actual field experiments, the inter-regional levelling operation resulted in a 47.5% reduction in the maximum elevation difference of the path, a 26.3% increase in the distribution of the 5 cm elevation difference, and a 53.7% improvement in flatness. In contrast, the refined levelling operation achieved a 62.9% reduction in the maximum elevation difference, a 52.0% increase in the distribution of the 5 cm elevation difference, and a 78.4% enhancement in flatness. These findings suggested that regional path planning could effectively enhance the area of fields meeting leveling standards, whereas fine-gridded path planning further optimized the leveling effect. This confirmed the efficacy of the scale-variable improved ant colony optimization algorithm in land leveling path planning.</p>

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Variable scale operational path planning for land levelling based on the improved ant colony optimization algorithm

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月21日

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  • Wenming Chen,
  • Jiaxin Yang,
  • Shaocen Zhang,
  • Xinhua Wei,
  • Chengliang Liu,
  • Xinying Zhou,
  • Lei Sun,
  • Fei Wang,
  • Anzhe Wang

摘要

To address the challenges associated with traditional traversal-based land leveling, which often suffered from a lack of coordination and planning, resulting in low operational efficiency and the management of large data volumes in contemporary path planning, this study proposed a variable-scale improved ant colony optimization algorithm specifically designed for land leveling path planning. The research introduced the IACO (Improved Ant Colony Optimization) algorithm for large-scale inter-region soil balance path planning, alongside the FIA*ACO (Fusion of the Improved A* Ant Colony Optimization) algorithm for small-scale, fine-grained full-field leveling path planning. Based on the analysis of actual field experiments, the inter-regional levelling operation resulted in a 47.5% reduction in the maximum elevation difference of the path, a 26.3% increase in the distribution of the 5 cm elevation difference, and a 53.7% improvement in flatness. In contrast, the refined levelling operation achieved a 62.9% reduction in the maximum elevation difference, a 52.0% increase in the distribution of the 5 cm elevation difference, and a 78.4% enhancement in flatness. These findings suggested that regional path planning could effectively enhance the area of fields meeting leveling standards, whereas fine-gridded path planning further optimized the leveling effect. This confirmed the efficacy of the scale-variable improved ant colony optimization algorithm in land leveling path planning.