改进混合Jaya优化算法求解柔性作业车间调度问题

Flexible job shop scheduling problem with improved hybrid Jaya optimization algorithm

针对柔性作业车间调度问题(Flexible Job shop Scheduling Problem,FJSP),以最小化最大完工时间为优化目标,在研究现有Jaya优化算法的基础上,结合邻域搜索算法,提出一种改进混合Jaya优化算法。首先,针对MSOS编码方式设计种群初始化方法;其次,提出一种基于Jaya优化算法思想的离散化更新算子,使算法适用于FJSP;然后,设计了2种新型邻域结构,有效增强了算法的局部寻优能力;最后,通过3组著名的FJSP基准算例进行测试,并与相同目标的其他算法进行对比分析。结果表明,改进混合Jaya优化算法能有效求解FJSP,且比相同目标的其他算法有更强的求解能力。

Based on the research of the existing Jaya optimization algorithm,an improved hybrid Jaya optimization algorithm combined with the neighborhood search algorithm was proposed for solving the Flexible Job shop Scheduling Problem(FJSP)with the objective to minimize makespan.Firstly,a population initialization method was designed for the MSOS encoding method.Secondly,to make the algorithm suitable for FJSP,a discretization update operator based on the idea of Jaya optimization was proposed.Thirdly,two new neighborhood structures were designed to improve the local optimization ability of the algorithm.Finally,it was tested through three sets of FJSP benchmarks,and compared with other algorithms which have the same target.The result shows that the improved hybrid Jaya optimization algorithm can effectively solve the flexible job shop scheduling problem,and it has better solving ability than other algorithms with the same goal.