A Two-Phase Differential Evolution for Uniform Designs in Constrained Experimental Domains
A Two-Phase Differential Evolution for Uniform Designs in Constrained Experimental Domains
复制标题
受限实验域中均匀设计的两阶段微分演化
DOI：
10.1109/tevc.2017.2669098
复制
发表时间：
2017-02
影响因子：
14.3
通讯作者：
Yang Shengxiang
中科院分区：
文献类型：
--
作者：
Wang Yong;Xu Biao;Sun Guangyong;Yang Shengxiang
In many real-world engineering applications, a uniform design needs to be conducted in a constrained experimental domain that includes linear/nonlinear and inequality/equality constraints. In general, these constraints make the constrained experimental domain small and irregular in the decision space. Therefore, it is difficult for current methods to produce a predefined number of samples and make the samples distribute uniformly in the constrained experimental domain. This paper presents a two-phase differential evolution for uniform designs in constrained experimental domains. In the first phase, considering the constraint violation as the fitness function, a clustering DE is proposed to guide the population toward the constrained experimental domain from different directions promptly. As a result, a predefined number of samples can be obtained in the constrained experimental domain. In the second phase, maximizing the minimum Euclidean distance among samples is treated as another fitness function. By optimizing this fitness function, the samples produced in the first phase can be scattered uniformly in the constrained experimental domain. The performance of the proposed method has been tested and compared with another state-of-the-art method. Experimental results suggest that our method is significantly better than the compared method in the uniform designs of a new type of automotive crash box and five benchmark test problems. Moreover, the proposed method could be considered as a general and promising framework for other uniform designs in constrained experimental domains.
登录
查看更多内容
DOI：
--
发表时间：
2015-12
期刊：
arXiv: Methodology
影响因子：
--
作者：
Shirin Golchi;Jason L. Loeppky
通讯作者：
Shirin Golchi;Jason L. Loeppky
DOI：
10.1016/s0169-7439(01)00139-3
发表时间：
2001-09-28
影响因子：
3.9
作者：
Liang, YZ;Fang, KT;Xu, QS
通讯作者：
Xu, QS
DOI：
10.1016/j.csda.2013.10.015
发表时间：
2014-04
期刊：
Comput. Stat. Data Anal.
影响因子：
--
作者：
Ray‐Bing Chen;Yen-Wen Hsu;Ying Hung;Weichung Wang
通讯作者：
Ray‐Bing Chen;Yen-Wen Hsu;Ying Hung;Weichung Wang
影响因子：
5.1
作者：
M. Anghileri;L. Castelletti;F. Invernizzi;M. Mascheroni
通讯作者：
M. Anghileri;L. Castelletti;F. Invernizzi;M. Mascheroni
具有复合试验向量生成策略和控制参数的差分进化
影响因子：
14.3
作者：
Wang, Yong;Cai, Zixing;Zhang, Qingfu
通讯作者：
Zhang, Qingfu