摘要： 随着无人机技术在农业领域的广泛应用，其高效作业策略的优化问题日益成为研究热点。针对农业喷洒作业中无人机受电池容量与药箱容量双重约束下的路径规划问题，引入多行程作业模式，以最小化作业成本为目标，构建了一个集成喷洒作业顺序优化、飞行路径规划和多机协同调度于一体的整数线性规划模型。为高效求解该复杂组合优化问题，设计了一种改进自适应大邻域搜索（ALNS）算法，结合问题特性构造了四种移除算子与三种插入算子，并引入了模拟退火（SA）算法作为劣解接受机制。通过计算算子得分，算法能够动态调整算子选择策略，从而提升求解性能。通过算法预实验确定了合理的参数设定。基于多组不同规模算例的数值实验表明，该算法在求解效率与解的质量上均显著优于商业求解器CPLEX与基于序列生成的方法。此外，将ALNS算法与主流启发式算法——遗传（GA）算法和蚁群（ACO）算法进行对比。实验结果表明，ALNS算法在中、大规模算例中的求解质量均显著优于GA和ACO算法。在中规模算例中，平均提升幅度分别为6.90%和3.55%；在大规模算例中，平均提升幅度分别为7.84%和4.47%。

Abstract: Unmanned aerial vehicle (UAV) is being extensively applied in agricultural operations, drawing increasing research attention to the optimization of efficient operational strategies. In agricultural spraying tasks, UAVs are constrained by both battery capacity and tank volume. To solve this problem, a multi-trip operation mode is introduced. The research formulates an integer linear programming model that simultaneously addressed three critical components: spraying sequence optimization, flight path planning, and multi-UAV scheduling coordination, with the objective of minimizing operational costs. To effectively solve this complex combinatorial optimization problem, an improved Adaptive Large Neighborhood Search algorithm is proposed. Four removal operators and three insertion operators are designed based on the characteristics of the problem. A Simulated Annealing mechanism is also used to accept worse solutions and improve global search ability. By calculating operator scores, the algorithm dynamically adjusts the operator selection strategy, thereby enhancing the solution performance. Parameter values are determined through preliminary experiments. Extensive computational experiments on benchmark instances of varying sizes demonstrate that the proposed algorithm significantly outperforms both the commercial solver CPLEX and sequence-based method in terms of solution quality and computational efficiency. Furthermore, the ALNS algorithm is compared with two mainstream metaheuristics—Genetic Algorithm (GA) and Ant Colony Optimization (ACO). The results show that ALNS consistently yields better solution quality on both medium-scale and large-scale instances. On medium-scale instances, it achieves average improvements of 6.90% over GA and 3.55% over ACO, while on large-scale instances, the improvements are 7.84% and 4.47%, respectively.