嵌套马尔科夫链超启发式 (NMHH):用于单一目标连续问题的混合超启发式框架
1Faculty of Mathematics and Computer Science, Babeş-Bolyai University, Cluj-Napoca, Romania.
PeerJ. Computer science
|March 4, 2024
概括
一个新的混合超启发性框架通过使用嵌套的马尔科夫链和模拟化来增强持续优化. 这种方法有效地识别了最佳的操作符序列和参数,在基准函数上表现优于现有的方法.
科学领域:
- 计算智能是一种计算智能.
- 优化算法优化算法
- 超听证学是一种超听证学.
背景情况:
- 持续优化问题带来了重大的计算挑战.
- 现有的超启发式框架需要进一步改进以获得最佳性能.
研究的目的:
- 引入一个新的混合超启发性框架,用于单一目标的持续优化.
- 在优化过程中增强操作员选择和参数调整.
主要方法:
- 基层的嵌套马尔科夫链识别了最佳的运算符序列.
- 在超级水平的模拟化演变了马尔科夫链及其参数.
- 全球和本地搜索运营商在马尔科夫链结构中的混合.
主要成果:
- 拟议的框架在基准持续优化问题上表现出卓越的性能.
- 数字实验证实了与其他超启发式和最先进的元启发式相比的有效性.
- 该方法成功地自动选择有效的操作员序列.
结论:
- 混合式超启发式框架为持续优化提供了有效的解决方案.
- 嵌套的马尔科夫链和模拟化集成提供了强大的性能.
- 这种方法推进了自动算法选择优化领域.
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