A deep reinforcement learning-guided memetic algorithm for multi-objective collaborative scheduling of agricultural weeding robot fleets.
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| Title: | A deep reinforcement learning-guided memetic algorithm for multi-objective collaborative scheduling of agricultural weeding robot fleets. |
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| Authors: | Zhang, Hui1 (AUTHOR) zhanghuishu@shu.edu.cn, Pan, Quanke1,2 (AUTHOR) panquanke@shu.edu.cn, Miao, Zhonghua1 (AUTHOR) zhonghuamiao@163.com, Zhu, Bo1 (AUTHOR) zhubo0015@shu.edu.cn |
| Source: | Computers & Electronics in Agriculture. Aug2026, Vol. 250, pN.PAG-N.PAG. 1p. |
| Subjects: | Scheduling, Multi-objective optimization, Metaheuristic algorithms, Precision farming, Markov processes, Reinforcement learning, Agricultural robots |
| Abstract: | • Multi-objective scheduling for agricultural weeding robot fleets. • Deep reinforcement learning-guided operator control enhances search stability. • Hybrid initialization with clustering accelerates convergence on large instances. • Markov decision process-based adaptation improves exploration–exploitation balance. • Improved HV and IGD over state-of-the-art baselines. The rapid deployment of intelligent unmanned systems is accelerating the transformation of precision agriculture. However, achieving autonomous, energy-efficient, and workload-balanced collaboration among heterogeneous weeding robot fleets in large-scale and structurally complex farmlands remains challenging. This study addresses a multi-objective collaborative scheduling problem for agricultural weeding robot fleets, aiming to minimize total travel distance, total energy consumption, and workload imbalance among robots. The problem is NP-hard, and conventional evolutionary approaches often suffer from limited adaptability due to static operator usage in complex search environments. To overcome these limitations, a deep reinforcement learning-guided memetic algorithm (DRL-MA) is developed. A dueling double deep Q-network is embedded within a Pareto-based memetic framework, where operator selection is formulated as a Markov decision process to enable adaptive exploration–exploitation balance. The framework further incorporates task-priority-based encoding, feasibility-preserving decoding, and a multi-stage hybrid heuristic initialization strategy to enhance convergence and solution diversity. Extensive experiments on benchmark smart-farm scenarios and a real-farm case study demonstrate that DRL-MA outperforms representative evolutionary, reinforcement-learning-assisted, and recent learning-based baselines. In the benchmark experiments, DRL-MA improves the mean hypervolume by approximately 11.7% and reduces the mean inverted generational distance by approximately 64.2% compared with the strongest competing algorithm. In the real-farm case study, DRL-MA contributes 78.31% of the globally non-dominated solutions, indicating stronger overall Pareto competitiveness. Moreover, runtime and scalability analyses show that the proposed method remains computationally feasible as the problem scale increases. These results indicate that DRL-MA provides an effective, scalable, and practically applicable scheduling approach for collaborative weeding robot fleets in smart farming. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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