Considering the low flexibility and efficiency of the scheduling problem, an improved multi-objective immune algorithm with non-dominated neighbor-based selection and Tabu search (NNITSA) is proposed. A novel Tabu search algorithm (TSA)-based operator is introduced in both the local search and mutation stage, which improves the climbing performance of the NNTSA. Special local search strategies can prevent the algorithm from being caught in the optimal solution. In addition, considering the time costs of the TSA, an adapted mutation strategy is proposed to operate the TSA mutation according to the scale of Pareto solutions. Random mutations may be applied to other conditions. Then, a robust evaluation is adopted to choose an appropriate solution from the obtained Pareto solutions set. NNITSA is used to solve the problems of static partitioning optimization and dynamic cross-regional co-operative scheduling of agricultural machinery. The simulation results show that NNITSA outperforms the other two algorithms, NNIA and NSGA-II. The performance indicator C-metric also shows significant improvements in the efficiency of optimizing search.

针对调度问题的灵活性和效率不高的问题，提出了一种改进的基于非支配邻居选择和禁忌搜索的多目标免疫算法（NNITSA）。在局部搜索和变异阶段引入了一种新的基于禁忌搜索算法 (TSA) 的算子，提高了 NNTSA 的爬升性能。特殊的局部搜索策略可以防止算法陷入最优解。此外，考虑到TSA的时间成本，提出了一种适应的变异策略，根据Pareto解的规模来操作TSA变异。随机突变可应用于其他条件。然后，采用稳健评估从获得的帕累托解集中选择合适的解。NNITSA用于解决农机静态分区优化和动态跨区域协同调度问题。仿真结果表明NNITSA优于其他两种算法NNIA和NSGA-II。性能指标 C-metric 也显示了优化搜索效率的显着改进。