基于混合人工蜂群算法的多目标柔性作业车间调度问题研究

传统的优化算法在求解面对多目标柔性作业车间调度时，往往求解效率低且难以获得最优解。为了求解多目标柔性作业车间调度问题，设计了混合人工蜂群算法。种群的初始化采用了多种方法相结合的策略。在人工蜂群算法的不同阶段采用不同的搜索机制，在雇佣蜂阶段采用开发搜索，针对跟随蜂阶段蜜蜂跟随的对象的优秀解进行小幅度的更新，从而提高了搜索的表现。禁忌搜索与改进的人工蜂群算法相结合，有效的提升了获得最优解的概率。通过相关文献中的标准实例对设计的混合人工蜂群算法进行一系列求解测试，实验的结果有效的说明了算法在求解柔性作业车间调度问题时效果显著。通过求解结果对比表明人工蜂群算法的高效性和优越性。

Study on multi-objective flexible Job-Shop scheduling problem based on hybrid artificial bee colony algorithm

When solving multi-objective flexible job shop scheduling problem, the traditional optimization algorithms were often inefficient and difficult to obtain the optimal solution. In order to solve the multi-objective flexible job shop scheduling problem, a hybrid artificial bee colony algorithm was designed. The method in population initialization phase was combination of several strategies. Different search mechanisms are employed at different stages of the artificial bee colony algorithm: the traditional exploit search was adopted in the employed bee phase, and objects of bees following were updated with little range in onlooker bee phase. The combination of tabu search and improved artificial bee colony algorithm effectively improved the probability of obtaining the optimal solution. The paper tested hybrid artificial bee colony algorithm through the Kacem instances. The results showed that the hybrid artificial bee colony algorithm is efficient in solving flexible job shop scheduling problem. The results indicated the efficiency and superiority of artificial bee colony algorithm.