基于教学优化算法求解置换流水车间调度问题

Teaching-Learning-Based Optimization Algorithm for Permutation Flowshop Scheduling

针对置换流水车间调度问题,将连续算法与离散策略相结合,提出一种多班级教学优化算法。采用基于置换变异改进的NEH(nawaz enscore ham)种群初始化方法,兼顾初始解的质量和多样性。在教学阶段,引入离散的自适应教学,并给出去重的操作,避免了无意义的教学过程。新增了基于莱维飞行的自学策略,同时以变邻域搜索的方式模拟离散阶段的自学。将相互学习与班级交流合并,在保证优秀个体交流的基础上,提高学习的效率。通过对标准测试集Rec进行测试,并与其他算法比较,验证了算法的有效性和稳定性。

A multi-classes teaching-learning-based optimization(MCTLBO)algorithm is proposed for the permutation flowshop scheduling problem(PFSP)by combining continuous algorithm with discrete strategy.An improved nawaz enscore ham(NEH)population initialization method based on permutation mutation is adopted,which takes into account the quality and diversity of initial solutions.In the teaching stage,discrete adaptive teaching with duplicate removal is introduced to avoid meaningless teaching processes.A new self-learning strategy based on Levy flight is added,and the self-learning in discrete stage is simulated by variable neighborhood search.Learner phase and class communication are combined to improve the efficiency of learning on the basis of ensuring the communication of excellent individuals.The standard test sets of Rec is tested,and compared with other algorithms,the validity and stability of the algorithm are verified.