关于多代理农业机械集团协作调度策略的研究
Ziyi Wang1, Fan Zhang2,3, Shiji Ma1
1College of Information Science and Technology, Hebei Agricultural University, Baoding, 071000, China.
Scientific reports
|March 17, 2025
概括
本研究介绍了一种基于深度强化学习 (MCMPP-DRL) 的多中心和多机器路径规划算法,以降低农业机械调度成本. 与传统方法相比,MCMPP-DRL算法大大降低了成本.
科学领域:
- 农业工程 农业工程
- 运营研究 运营研究
- 人工智能的人工智能
背景情况:
- 高调度成本和低效率困扰着跨多个调度中心的合作农业机械操作.
- 优化资源配置对于提高现代农业生产率至关重要.
研究的目的:
- 开发一个先进的调度模型,最大限度地降低多中心,多机器农业操作的总成本.
- 引入和评估基于深度强化学习 (MCMPP-DRL) 的多中心和多机器路径规划算法的有效性.
主要方法:
- 建立了基于注意力的政策网络的深度强化学习环境.
- 使用REINFORCE政策梯度算法训练网络.
- 通过本地搜索策略优化解决方案,并进行比较分析.
主要成果:
- 与群优化 (ACO),模拟化 (SA) 和遗传算法 (GA) 相比,MCMPP-DRL算法显示出显著的成本降低.
- 实现的成本削减至少为9.66% (与ACO相比),14.34% (与SA相比) 和24.41% (与GA相比).
- 该算法在河北省玉米种植区的各种农田规模 (20-120) 中进行了测试.
结论:
- MCMPP-DRL算法在复杂农业操作的调度成本效率方面提供了实质性的改进.
- 这种方法为优化多中心,多机器调度问题提供了强大的理论基础和技术支持.
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