一个辐射基础函数方法,用于噪音全球优化
Dirk Banholzer1, Jörg Fliege1, Ralf Werner2
1Department of Mathematical Sciences, University of Southampton, Southampton, SO17 1BJ UK.
概括
本研究引入了一种新的响应表面方法,用于优化使用误差界限的复杂,杂的函数. 这种新的方法增强了昂贵的黑子函数的全球优化策略.
科学领域:
- 数字分析 数字分析
- 优化优化 优化优化
- 计算数学是指计算数学.
背景情况:
- 全球优化昂贵和杂的黑子功能是一个挑战.
- 像古特曼的辐射基函数 (RBF) 方法这样的现有方法对于确定性函数是有效的,但需要适应杂的场景.
- 对于噪音函数评估的错误极限的可用性对于强大的优化至关重要.
研究的目的:
- 开发一种新的响应表面方法,用于全球优化昂贵和杂的黑盒客观函数.
- 将可用的错误界限纳入优化过程,以提高准确性和可靠性.
- 扩展Gutmann的RBF方法的原则,以处理噪音功能评估.
主要方法:
- 拟议的方法使用规则化的最小平方标准来构建辐射基函数 (RBF) 的近似值.
- 它根据目标值确定连续评估的新样本点,类似于最初的RBF方法.
- 噪音函数值的误差极限被整合到近似和采样策略中.
主要成果:
- 该方法为昂贵,杂的目标函数的全球优化提供了一个强大的框架.
- 确定了新型响应表面方法的收性质.
- 一个测试问题的数值示例表明了该方法的实际应用性.
结论:
- 开发的响应表面方法有效地处理昂贵和杂的黑子优化问题.
- 错误界限的整合提高了全球优化过程的可靠性.
- 该方法为现有的基于RBF的优化技术提供了有价值的扩展.
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