中国机械工程

• 智能制造 • 上一篇    下一篇

基于改进人工鱼群算法的柔性作业车间调度

赵敏1;殷欢1;孙棣华1;郑林江1;何伟1;袁川2#br#   

  1. 1.重庆大学,重庆,400030
    2.重庆信息安全测评中心,重庆,401147
  • 出版日期:2016-04-25 发布日期:2016-04-26
  • 基金资助:
    国家自然科学基金资助项目(61203135);中央高校基本科研业务费专项资金资助项目(106112014CDJZR178801);重庆市自然科学基金资助项目(CSCT2012JJA40020)

Flexible Job Shop Scheduling Based on Modified Artificial Fish Swarm Algorithm

Zhao Min1;Yin Huan1;Sun Dihua1;Zheng Linjiang1;He Wei1;Yuan Chuan2   

  1. 1.Chongqing University,Chongqing,400030
    2. Chongqing Information Technology Security Evaluation Center, Chongqing,401147
  • Online:2016-04-25 Published:2016-04-26
  • Supported by:

摘要: 提出了一种基于改进人工鱼群算法的柔性作业车间调度问题的求解方法。该方法针对基本人工鱼群算法后期搜索盲目性大、精度不高的不足,在分析算法各个参数影响的基础上,提出了步长参数分解和采用柔性参数设置等改进策略,并在算法后期融入局部遍历搜索,提高了算法寻优能力和寻优精度。标准MK算例和对比试验表明了改进人工鱼群算法对求解柔性作业车间调度问题的有效性。

关键词: 柔性作业车间调度, 人工鱼群算法, 柔性参数设置, 参数细分

Abstract: An improved algorithm for solving flexible job shop scheduling was proposed based on the artificial fish swarm algorithm. In view of the blindness and low precision of the basic artificial fish swarm algorithm in its late stage search, the new algorithm presented some strategies like separating the step into the random moving step and target moving step, adopting flexible parameter setting, and infusing local traversal search in the late period of the algorithm based on the analyses of parameter influences of the algorithm, which enhanced the search ability and search precision of the modified algorithm. Finally, the effectiveness of the improved artificial fish swarm algorithm in solving flexible job shop scheduling problem was verified by the standard MK sample and comparison experiments.

Key words: flexible job shop scheduling, artificial fish swarm algorithm, flexible parameter setting, parameter subdivision

中图分类号: