基于改进的殖民地优化算法进行土地平整的可变规模操作路径规划
Wenming Chen1, Jiaxin Yang1, Shaocen Zhang1
1School of Agricultural Engineering, Jiangsu University, Zhenjiang, China.
Scientific reports
|March 22, 2025
概括
本研究介绍了先进的殖民地优化算法,用于高效地平面化路径规划. 这些方法显著提高了田地平坦度,减少了海拔差异,优化了农业用地管理.
科学领域:
- 农业工程 农业工程
- 计算智能是一种计算智能.
- 地理学就是地质学.
背景情况:
- 传统的土地平面化方法在协调,规划和数据管理方面面临挑战,导致效率低.
- 土地平面化路径规划需要高效的算法来处理大型数据集并优化运营结果.
研究的目的:
- 为土地平面化路径规划提出和评估一个变量级改进的殖民地优化算法.
- 通过优化路径规划,提高运营效率和改善农田的平面性.
主要方法:
- 开发了改进的殖民地优化 (IACO) 算法,用于大规模的区域间土壤平衡.
- 引入了改进的A*殖民地优化 (FIA*ACO) 的融合,用于小规模的,细粒度的全场平衡.
- 进行了实地实验,以分析拟议算法的性能.
主要成果:
- 区域间平面化将最大海拔差距降低了47.5%,并提高了53.7%的平面性.
- 细粒度平衡使最大高度差异减少了62.9%,并增加了78.4%的平坦度.
- 区域和精细网格路径规划都显示了平面化标准和效果的显著改善.
结论:
- 变量级改进的群优化算法有效地解决了土地平面化路径规划中的挑战.
- 区域路径规划提高了符合平面化标准的田地面积,而精细的网格规划优化了平面化效应.
- 拟议的算法为改善农业用地管理和效率提供了强大的解决方案.
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