基于非支配排序遗传算法的多农机协同任务规划

doi:10.13733/j.jcam.issn.2095-5553.2025.06.017

摘要/Abstract

摘要：

针对农田环境中多农机协同作业存在效率低、作业时间长等问题，提出一种基于非支配排序遗传算法（NSGA）的多农机协同作业任务规划方法。根据农机的实际工作模式，在考虑机群的作业能力、工作时间以及其他成本的情况下建立多机协同的成本函数。为避免优化算法陷入局部最优，构建非支配排序遗传算法，设计均匀交叉算子和反转变异算子。该方法综合农机作业任务中时间约束和资源限制之间的相互关系，建立一个多机型单任务协同优化调度模型，并引入非支配排序遗传算法来优化目标函数，在任务规划中追求全局最佳解决方案。仿真试验结果表明，在任务数量为11、22、33、44时，基于非支配排序遗传算法比传统遗传算法的任务总路径长度分别减少19.7%、11.4%、17.5%、18.9%。

关键词: 多机协同作业, 任务分配, 非支配排序, 遗传算法, 农业机械

Abstract:

A task planning method for multi‑machine collaborative operations in agricultural environments was developed using anon‑dominated sorting genetic algorithm (NSGA)to address issues of low efficiency and prolonged operation times. A cost function for multi‑machine collaboration was formulated based on the actual working modes of agricultural machinery.This function incorporated operational capacity, working time, and associated costs of the machine cluster. To prevent the optimization process from converging to local optima, a customized NSGA was constructed, featuring uniform crossover operators and reverse mutation operators. The proposed method integrated interdependencies between time and resource constraints in agricultural machinery operations, establishing a collaborative optimization scheduling model for multiple machines performing a single task. The NSGA was employed to optimize the objective function, aiming for aglobal optimal solution in task planning. The simulation results showed that when the number of tasks was 11, 22, 33 and 44, the total travel distance of NSGA was reduced by 19.7%, 11.4%, 17.5% and 18.9%, respectively, compared to the traditional genetic algorithm.

Key words: multi machine collaborative operation, task planning, non?dominated sorting, genetic algorithm, agricultural machinery

中图分类号:

S220

邓瑞, 郭旺, 陈雯柏, 赵春江, . 基于非支配排序遗传算法的多农机协同任务规划[J]. 中国农机化学报, 2025, 46(6): 113-119.

Deng Ru, , Guo Wang, , , Chen Wenbai, Zhao Chunjiang, , . Multi agricultural machinery collaborative task planning based on non‑dominated sorting genetic algorithm[J]. Journal of Chinese Agricultural Mechanization, 2025, 46(6): 113-119.

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