.png){kind=icon}
返回
Evolution Control for parallel ANN-assisted simulation-based optimization application to Tuberculosis Transmission Control
.png){kind=icon}
OA
AI
DOI：10.1016/j.future.2020.07.005.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In many optimal design searches, the function to optimize is a simulator that is computationally expensive. While current High Performance Computing (HPC) methods are not able to solve such problems efficiently, parallelism can be coupled with approximate models (surrogates or meta-models) that imitate the simulator in timely fashion to achieve better results. This combined approach reduces the number of simulations thanks to surrogate use whereas the remaining evaluations are handled by supercomputers. While the surrogates' ability to limit computational times is very attractive, integrating them into the overarching optimization process can be challenging. Indeed, it is critical to address the major trade-off between the quality (precision) and the efficiency (execution time) of the resolution. In this article, we investigate Evolution Controls (ECs) which are strategies that define the alternation between the simulator and the surrogate within the optimization process. We propose a new EC based on the prediction uncertainty obtained from Monte Carlo Dropout (MCDropout), a technique originally dedicated to quantifying uncertainty in deep learning. Investigations of such uncertainty-aware ECs remain uncommon in surrogate-assisted evolutionary optimization. In addition, we use parallel computing in a complementary way to address the high computational burden. Our new strategy is implemented in the context of a pioneering application to Tuberculosis Transmission Control. The reported results show that the MCDropout-based EC coupled with massively parallel computing outperforms strategies previously proposed in the field of surrogate-assisted optimization. (c) 2020 Elsevier B.V. All rights reserved.
Keyword:
Surrogate-assisted optimization
Simulation
Evolution Control
Artificial Neural Network
Massively parallel computing
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
F
IF:
6.1
论文数:
6.8K
被引数:
2.3W
机构
引用论文
Surrogate based optimization of helicopter rotor blades for vibration reduction in forward flight基于代理的直升机旋翼桨叶前向飞行减振优化
PREAI
分享收藏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Establishment of specific SSR from different linkage groups of cabbage compared with Chinese cabbage
IF0
PREAI
分享收藏Cross-linkable Polymer Matrix for Enhanced Thermal Stability of Succinonitrile-based Polymer Electrolyte in Lithium Rechargeable Batteries可交联聚合物基质，用于增强锂可充电电池中基于丁二腈的聚合物电解质的热稳定性OAAI
OAAI分享收藏A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem数据驱动的代理辅助进化算法应用于多目标高炉优化问题OAAI
OAAI分享收藏
查看更多内容