| 引用本文: | 李泷鑫,桑红燕,孟磊磊,张彪.基于分组策略的多除草机器人任务分配多目标优化[J].控制理论与应用,2025,42(11):2322~2331.[点击复制] |
| LI Long-xin,SANG Hong-yan,MENG Lei-lei,ZHANG Biao.Multi-objective optimization of task allocation for multiple weeding robots based on grouping strategy[J].Control Theory & Applications,2025,42(11):2322~2331.[点击复制] |
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| 基于分组策略的多除草机器人任务分配多目标优化 |
| Multi-objective optimization of task allocation for multiple weeding robots based on grouping strategy |
| 摘要点击 3705 全文点击 150 投稿时间：2025-03-27 修订日期：2025-10-12 |
| 查看全文 查看/发表评论 下载PDF阅读器 HTML |
| DOI编号 10.7641/CTA.2025.50125 |
| 2025,42(11):2322-2331 |
| 中文关键词 多机器人任务分配 人工蜂群算法 多目标优化 分组策略 |
| 英文关键词 multi-robot task allocation artificial bee colony algorithm multi-objective optimization grouping strategy |
| 基金项目 国家自然科学基金项目(62473186,62273221,52205529), 山东省自然科学基金项目(ZR2024MF017)资助. |
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| 中文摘要 |
| 本文针对多机器人除草任务分配问题(MWRTA)展开研究,旨在最小化任务最大完成时间、总能耗以及总
农药剩余,这些指标是衡量可持续农业系统性能的关键因素.为此,本文建立了一个混合整数线性规划模型,并提出
了一种新颖的基于分组策略的多目标离散人工蜂群算法(GMO-DABC),以高效求解MWRTA问题.首先,设计了一
种融合分组策略与负载均衡的启发式方法,用于有效生成解;其次,基于分组策略构建邻域算子结合问题知识动态
调整邻域结构,降低陷入局部最优的风险;最后,提出了一种结合分组策略与非支配前沿分析的搜索策略,以高效探
索解空间.在多个规模的算例中进行的大量仿真实验表明,GMO-DABC在解质量、收敛速度和鲁棒性方面均优于
多种先进算法,验证了该算法在优化能力方面的优势及其在实际农业应用中的潜在价值. |
| 英文摘要 |
| This paper addresses the multiple weeding robots task allocation (MWRTA) problem, aiming to minimize
the maximum task completion time, total energy consumption, and the amount of residual pesticides, which are key perfor
mance indicators in sustainable agricultural systems. A mixed integer linear programming (MILP) formulation is proposed,
and a novel grouping strategy-based multi-objective discrete artificial bee colony algorithm (GMO-DABC) is developed for
solving the MWRTA problem efficiently. Firstly, heuristic methods integrating grouping strategy with load balancing is
designed to effectively generate solutions. Secondly, neighborhood operators are designed based on the grouping strategy,
dynamically adjusting neighborhood structures by knowledge-guided to reduce the risk of local optimum. Finally, a search
strategy combining grouping strategy with non-dominated frontier analysis is proposed to efficiently explore the solution
space. Extensive simulation experiments under multiple instance scales validate the superiority of GMO-DABC over several
state-of-the-art algorithms in terms of solution quality, convergence speed and robustness, confirms its strong optimization
capability and practical value for real-world agricultural applications. |
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