求解PFSP的双种群协同学习算法

Double population co-learning algorithm for permutation flow-shop scheduling problems

在人工蜜蜂群算法的基础上,提出一种双种群协同学习算法.该算法根据个体适应度高低把蜜蜂群划分为两个子群,并重新定义子群的学习交流机制.在10个常用的基准测试函数上与其他4个常用的群体智能算法进行比较,比较结果表明,所提出算法的性能有明显改进.采用双种群协同学习算法求解置换流水车间调度问题,在一些著名的中大规模测试问题包括21个Reeves实例和40个Taillard实例上进行测试,结果表明,所提出的算法优于其他算法,能有效解决置换流水车间调度问题.

Based on the artificial bee colony（ABC） algorithm, a double population co-learning（DPCL） algorithm is proposed. A population is divided into two populations according to their fitness. The individuals of each population are updated according to the given learning rules. With a test on ten benchmark functions, the proposed DPCL algorithm is proved to have significant improvement over canonical ABC and several other comparison algorithms. The DPCL algorithm is then employed for permutation flow-shop scheduling problem（PFSP）. Twenty-one Reeves instances and forty Taillard instances are used. The results show that the DPCL algorithm can obtain better results than other algorithms, and is a competitive approach for PFSP.