Computer Engineering and Applications ›› 2018, Vol. 54 ›› Issue (23): 259-263.DOI: 10.3778/j.issn.1002-8331.1708-0297

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Cat swarm optimization for solving flexible job shop scheduling problem

JIANG Tianhua   

  1. School of Transportation, Ludong University, Yantai, Shandong 264025, China
  • Online:2018-12-01 Published:2018-11-30



  1. 鲁东大学 交通学院,山东 烟台 264025

Abstract: According to the production characteristics of the flexible job shop, the original cat swarm optimization algorithm is designed and improved to propose an Improved Cat Swarm Optimization(ICSO), which is used to optimize the makespan of the workshop. Firstly, a two-phase individual position encoding method and a heuristic-based population initialization strategy are given in the algorithm. Second, an adaptive selection method of behavior modes is employed to effectively coordinate the global search and local search of the algorithm. Then, a seeking mode based on diversified seeking operator is developed to enhance the global search ability. A tracking mode based on the levy flight is proposed to improve the local search ability. In addition, a leaping mechanism is introduced to further improve the performance of the algorithm. Experimental data demonstrate that the ICSO is effective for solving the FJSP.

Key words: flexible job shop, production scheduling, makespan, improved cat swarm optimization

摘要: 根据柔性作业车间的生产特点,对基本猫群优化算法进行设计和改进,提出了一种改进型猫群优化算法(Improved Cat Swarm Optimization,ICSO),用于优化车间内工件的最大完工时间。算法给出了两段式个体位置编码方式和基于启发式算法的种群初始化策略;采用自适应行为模式选择方法,使其能够有效协调算法全局和局部搜索;提出了基于多样化搜寻算子的搜寻模式,增强算法的全局搜索能力;提出了基于莱维飞行的跟踪模式,增强算法的局部搜索能力。此外,算法中还引入了跳跃机制,使算法性能能够得到进一步的改善。实验数据表明ICSO算法在求解FJSP问题方面具有一定的有效性。

关键词: 柔性作业车间, 生产调度, 最大完工时间, 改进猫群优化算法