一个结合的尖端神经P系统与两个层次的社区搜索集成,用于解决灵活的工作场所调度问题
Xiang Tian1, Yang Kong1, Feng Zhang2
1School of Health Management, Binzhou Medical University, Yantai, Shandong, 264003, China.
一个新的Coupled Spiking Neural P系统 (CSNP) 解决了灵活的工作车间调度问题 (FJSP). 这种方法在基准实例上取得了最先进的结果,大大提高了调度效率.
科学领域:
- 运营研究 运营研究
- 人工智能的人工智能
- 计算优化计算优化
背景情况:
- 灵活的工作车间调度问题 (FJSP) 是一个NP难题,具有复杂的实例,挑战现有的元启发学.
- 局限性包括连续搜索,冗余移动,以及不良的勘探-开发平衡,阻碍了可扩展性和稳定性.
研究的目的:
- 为解决大规模和高度灵活的FJSP实例开发先进的元启发术.
- 为了提高复杂的优化问题的调度算法的可扩展性和稳定性.
主要方法:
- 提出了一种结合尖端神经P系统 (CSNP),集成了一种遗传算法 (GA) 模块和一个双层邻里搜索.
- 采用并行膜计算来提高计算效率.
- 利用机器间的移动 (不重叠的关键操作) 和机器内部的移动 (反序关键块).
主要成果:
- 在49个基准FJSP实例中,为47个实现了当前最知名的解决方案.
- 改进了16个历史最佳解决方案,展示了卓越的性能.
- 废弃性研究证实了模块的有效性:GA (≈56.4%),机器间移动 (≈18.6%) 和机器内部移动 (≈16.8%) 对总增益7.85%有显著的贡献.
结论:
- 该CSNP框架有效地解决了FJSP现有的元启发术的局限性.
- 提出的方法展示了快速,稳定和可扩展的搜索行为,优于以前的方法.
- 对GA和专业邻里搜索的协同集成是提高性能的关键.
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