目的 为了进一步优化航材库存结构，解决多目标航材分配问题，提高航材保障工作决策的效率。方法 建立基于费用和分配满意度的多目标航材分配模型，运用先进的群体智能算法——蝗虫算法求解。结果 算例分析表明，在3种求解算法中，蝗虫算法所求出来的解，既使得航材分配过程中所需成本最低，又保证了航材股满意度处于较高水平。同时，将算法运行10次，蝗虫算法的求解时间平均值和方差分别为4.01 ms和11.5 ms，明显优于传统的群智能算法粒子群和NSGA-Ⅱ算法的求解效率。结论 蝗虫算法能够有效地解决多目标航材分配问题，对于优化航材库存，平衡航材数量具有重要的现实意义。

The work aims to optimize air material inventory structure and solve the problem of multi-objective air meterial allocation which is helpful to improve the efficiency of air material support decision-making. A multi-objective air material allocation model was established based on cost and allocation satisfaction. The advanced intelligent algorithm-GOA was adopted to solve it. The example analysis showed that among the three algorithms, the results solved by grasshopper optimization algorithm not only minimized the cost in air material allocation, but also guaranteed high satisfaction of air material stocks. At the same time, the three algorithms were executed ten times. The mean and variance of grasshopper optimization algorithm were 4.01 ms and 11.5 ms. It was evidently superior to solution efficiency of the traditional group of particle swarm intelligence algorithm and the NSGA-Ⅱ algorithm. GOA can effectively solve the problem with the allocation of multi-objective air materials. It has practical significance for optimizing air material inventory and balancing air material quantity.