Surrogate-Based Methods
Surrogate-Based Methods
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DOI：
10.1007/978-3-642-20859-1_3
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
S. Koziel;D. E. Ciaurri;Leifur Þ. Leifsson
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文献类型：
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作者：
S. Koziel;D. E. Ciaurri;Leifur Þ. Leifsson
在工程实践中出现的目标函数可能来自物理系统的测量，更常见的是来自计算机模拟。在许多情况下，以一种直接的方式对这些目标进行优化，即直接对这些函数应用优化例程，是不切实际的。一个原因是基于模拟的目标函数通常是难以解析的（不连续的、不可微的、固有的噪声）。此外，敏感性信息通常是不可用的，或者计算成本太高。另一个更重要的原因是测量/模拟的高计算成本。尽管可用的计算能力不断提高，但在当代工程中，每个目标函数评估的模拟时间长达数小时、数天甚至数周并不罕见。对这些难以管理的函数的可行处理可以使用代理模型来完成：原始目标的优化被迭代的重新优化和更新解析上易于处理和计算上便宜的代理所取代。本章简要介绍了基于代理的优化的基础知识，创建代理模型的各种方法，以及基于代理的优化技术的几个例子。
Objective functions that appear in engineering practice may come from measurements of physical systems and, more often, from computer simulations. In many cases, optimization of such objectives in a straightforward way, i.e., by applying optimization routines directly to these functions, is impractical. One reason is that simulation-based objective functions are often analytically intractable (discontinuous, non-differentiable, and inherently noisy). Also, sensitivity information is usually unavailable, or too expensive to compute. Another, and in many cases even more important, reason is the high computational cost of measurement/simulations. Simulation times of several hours, days or even weeks per objective function evaluation are not uncommon in contemporary engineering, despite the increase of available computing power. Feasible handling of these unmanageable functions can be accomplished using surrogate models: the optimization of the original objective is replaced by iterative re-optimization and updating of the analytically tractable and computationally cheap surrogate. This chapter briefly describes the basics of surrogate-based optimization, various ways of creating surrogate models, as well as several examples of surrogate-based optimization techniques.