在分布式变量流车间调度问题和连续工程问题中应用一种由亚当梯度下降启发的新型元启发算法
Yiqiang Xia1, Yanzhe Ji2,3
1College of Science, Liaoning Technical University, Fuxin, 123000, China. xiayiqiang0001@163.com.
Scientific reports
|July 2, 2025
概括
一个新的Adam渐变下降优化器 (AGDO) 平衡了复杂的优化问题的探索和利用. AGDO在基准和现实世界的工程挑战方面表现出卓越的表现,包括DPFSP.
科学领域:
- 优化算法 优化算法
- 计算智能是一种计算智能.
- 数学优化的数学优化
背景情况:
- 群体智能算法通常由于类似的设计而表现出不平衡的探索和利用.
- 数学属性与随机过程相结合可以提高优化.
- 现有的元启发学与复杂的持续优化和工程挑战作斗争.
研究的目的:
- 介绍亚当梯度下降优化器 (AGDO),一个新的元启发式算法.
- 解决当前优化技术中勘探开发平衡的局限性.
- 评估AGDO在持续优化和工程问题上的有效性.
主要方法:
- 开发的AGDO灵感来自于Adam优化器,结合了三个关键规则:渐进梯度动量集成,动态梯度交互系统和系统优化操作员.
- 在多个维度 (10,30,50,100) 上对CEC2017基准的19个既定和新型的元启发式数据进行了AGDO绩效评估.
- 评估了AGDO在六个实用工程挑战上,包括分布式转换流车间调度问题 (DPFSP),并将其与六个最先进的算法进行了比较.
主要成果:
- 在10,30,50和100个维度中,AGDO表现出强的表现,在三个维度中获得了最高的Wilcoxon等级和值测试分数.
- 该算法在勘探和开采之间保持了出色的平衡,快速汇聚,并回避了局部最佳.
- 在复杂的现实工程挑战中,AGDO表现出显著的有效性,特别是在分布式转换流量车间调度问题 (DPFSP) 中表现出色.
结论:
- 通过有效平衡勘探和开采,AGDO为持续优化和工程挑战提供了卓越的方法.
- 算法的数学基础和新型运算符有助于其强大的性能和逃避局部最佳的能力.
- AGDO代表了元启发式优化的重大进步,对复杂的调度和工程任务具有实际意义.
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