改进人工蜂群求解多目标柔性作业车间调度问题

针对多个目标约束的柔性作业车间问题，本文采用基于Pareto解集的改进离散人工蜂群算法来求解.由于经典人工蜂群算法的选择概率不适用于多目标问题，本文对选择概率进行了重定义，将排序引入选择概率中；同时采用基于变异操作的邻域搜索方法进行局部搜索，并使用混合列交叉算子提高种群的多样性；采用Harmonic平均距离对Pareto解集进行裁剪，完成对Pareto解集的更新.最后通过实例测试及仿真实验，验证了本文算法在求解多目标柔性作业车间调度时的有效性.

Improved Artificial Bee Colony Algorithm for Solving Multi-objective Flexible Job-shop Scheduling Problem

In this paper, we propose an improved discrete artificial bee colony algorithm based on the Pareto solution to solve the problem of a flexible job shop with multiple target constraints. Since the selection probability for classical artificial colonies is not applicable to multi-objective problems, we redefine the selection probability to depend on ranking. We also use the neighborhood search method based on a mutation operation for the local search, and apply a hybrid-column crossover operator to improve population diversity. Then, we attach the Pareto solution set to the harmonic average distance and update the Pareto solution set. We verified the effectiveness of the proposed algorithm in solving the multi-objective flexible job-shop scheduling problem in a case test and simulation experiment.