一个模糊的专家增强了NSGA-II方法,用于可持续的农业系统
Zhonglin Huang1, Yongjun Pu2, Qianrong Zhang3
1School of Network and Communication Engineering, Chengdu Technological University, Chengdu, 611730, China.
Scientific reports
|October 1, 2025
概括
一个新的Fuzzy-Expert-NSGA-II算法优化了农业种植,以获得利和可持续性. 这种先进的方法平衡了经济收益与生态健康,为土地利用规划提供了数据驱动的解决方案.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 环境科学 环境科学
背景情况:
- 优化农业种植战略需要平衡经济效益,生态可持续性和管理效率.
- 现有的多目标优化方法可能无法完全捕捉复杂的农业约束和专家知识.
研究的目的:
- 开发和评估一个增强的多目标优化算法,Fuzzy-Expert-NSGA-II,用于农业种植战略.
- 将模糊的专家系统与进化计算相结合,以改善农业的决策支持.
主要方法:
- 开发了一个模糊-专家-NSGA-II算法,这是NSGA-II的增强,结合了模糊的专家系统.
- 为中国河村创建了一个多期作物规划模型,考虑了41种作物类型和54个具有各种限制的地块.
- 引入混合适应本地搜索 (HALS) 和基于专家规则的修复 (ERB) 模块,以提高解决方案的质量和可行性.
主要成果:
- 模糊-专家-NSGA-II算法超过了标准的NSGA-II,MOPSO和MOEA/D,达到0.892的超量指标和1.2%的约束满足率.
- 优化种植策略预计将在2024-2030年期间将平均利提高23%.
- 保持高的生物多样性水平,辛普森指数从0.72到0.83.8不等.
结论:
- 模糊-专家-NSGA-II算法为农业用地规划提供了有效的数据驱动决策支持工具.
- 拟议的框架成功地平衡了经济目标与农业管理中的生态可持续性.
- 这项研究通过整合专家知识和先进的计算方法,为农业中复杂的优化问题提供了一种新的方法.
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