Time dependent sheet metal forming optimization by using Gaussian process assisted firefly algorithm
Time dependent sheet metal forming optimization by using Gaussian process assisted firefly algorithm
复制标题
使用高斯过程辅助萤火虫算法进行时间相关的钣金成形优化
DOI：
10.1007/s12289-017-1352-9
复制
发表时间：
2018-03
影响因子：
2.4
通讯作者：
Li Enying
中科院分区：
文献类型：
--
作者：
Wang Hu;Chen Lei;Li Enying
在板料成形优化问题中，很少考虑与时间相关的设计变量。这项工作的目的是处理与时间相关的金属板材成形问题。由于难以调查整个成形过程中的所有时间点，因此需要提取一些关键时间点。因此，设计变量的数量应显着增加，由于引入辅助时间设计变量。然而，维数灾是一个难以解决的难题。针对这类中等规模的问题，提出了高斯过程辅助萤火虫算法（GPFA）。该方法的主要思想是利用萤火虫算法（FA）构建一个代理模型感知的搜索机制，以有效地进行基于仿真的优化。与其他FA相比，GPFA的显著特点是基于最大期望改进（EI）准则自适应地产生新的样本点，从而能够很好地平衡局部和全局搜索，对20个变量的基准问题和一个真实的-世界范围内与时间相关的板料成形优化的应用表明，GPFA能够解决类似的问题。
For a sheet metal forming optimization problem, time related design variables are seldom considered in practice. The purpose of this work is to handle time dependent sheet metal forming problems. Because it is difficult to investigate all time points during the entire forming procedure, some key time points should be extracted. Therefore, the number of design variables should be significantly increased due to introduce auxiliary time design variables. However, curse of dimensionality is a formidable difficult issue to be solved. To solve such medium-scale problems, Gaussian Process Assisted Firefly Algorithm (GPFA) is suggested. The main idea of the suggested method is to construct a surrogate model-aware search mechanism with Firefly Algorithm (FA) for simulation-based optimization efficiently. Compared with other FAs, the distinctive characteristic of GPFA is to generate new sample points adaptively based on maximum Expected Improvement (EI) criterion, so that the local and global search can be well balanced, and a small promising area can be quickly focused on. Numerical studies on benchmark problems with 20 variables and a real-world application of time dependent sheet metal forming optimization reveal that the GPFA is capable to solve such similar problems.
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影响因子：
6.3
作者：
T. Ohata;Yasunori Nakamura;Tutao Katayama;E. Nakamachi
通讯作者：
T. Ohata;Yasunori Nakamura;Tutao Katayama;E. Nakamachi
DOI：
10.1145/2598394.2605342
发表时间：
2014-07
期刊：
Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation
影响因子：
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作者：
A. Engelbrecht
通讯作者：
A. Engelbrecht
DOI：
10.1007/s00170-008-1383-y
发表时间：
2009-02
期刊：
The International Journal of Advanced Manufacturing Technology
影响因子：
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作者：
Jyhwen Wang;A. Goel;Fengchen Yang;J. Gau
通讯作者：
Jyhwen Wang;A. Goel;Fengchen Yang;J. Gau
DOI：
10.1016/b978-0-12-409547-2.14581-0
发表时间：
2020
期刊：
Comprehensive Chemometrics
影响因子：
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作者：
Federico Marini;Beata Walczak
通讯作者：
Federico Marini;Beata Walczak
DOI：
10.1016/j.ress.2008.02.024
发表时间：
2005-08
期刊：
Reliab. Eng. Syst. Saf.
影响因子：
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作者：
Wenfeng Zhang;R. Shivpuri
通讯作者：
Wenfeng Zhang;R. Shivpuri