.png){kind=icon}
返回
Adaptive in situ model refinement for surrogate-augmented population-based optimization
.png){kind=icon}
PRE
AI
DOI：10.1007/s00158-020-02592-6.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In surrogate-based optimization (SBO), the deception issues associated with the low fidelity of the surrogate model can be dealt with in situ model refinement that uses infill points during optimization. However, there is a lack of model refinement methods that are both independent of the choice of surrogate model (neural networks, radial basis functions, Kriging, etc.) and provides a methodical approach to preserve the fidelity of the search dynamics, especially in the case of population-based heuristic optimization processes. This paper presents an adaptive model refinement (AMR) approach to fill this important gap. Therein, the question of when to refine the surrogate model is answered by a novel hypothesis testing concept that compares the distribution of model error and distribution of function improvement over iterations. These distributions are respectively computed via a probabilistic cross-validation approach and by leveraging the probabilistic improvement information uniquely afforded by population-based algorithms such as particle swarm optimization. Moreover, the AMR method identifies the size of the batch of infill points needed for refinement. Numerical experiments performed on multiple benchmark functions and an optimal (building energy) planning problem demonstrate AMR's ability to preserve computational efficiency of the SBO process while providing solutions of more attractive fidelity than those provisioned by a standard SBO approach.
Keyword:
Adaptive model refinement
Surrogate-based optimization
Predictive estimation of model fidelity
Sequential sampling
Particle swarm optimization
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4
论文数:
4.9K
被引数:
1.7W
机构
引用论文
Rate of acyl migration in lysophosphatidylcholine (LPC) is dependent upon the nature of the acyl group. Greater stability of sn-2 docosahexaenoyl LPC compared to the more saturated LPC species.png)
PLOS ONE
OAAI
PLOS ONEIF0
OAAI分享收藏Impacts of building geometry modeling methods on the simulation results of urban building energy models建筑几何建模方法对城市建筑能耗模型仿真结果的影响APPLIED ENERGY
OAAI
APPLIED ENERGYIF11
OAAI分享收藏
查看更多内容