期刊
NEURAL COMPUTING & APPLICATIONS
卷 33, 期 24, 页码 17131-17145出版社
SPRINGER LONDON LTD
DOI: 10.1007/s00521-021-06304-z
关键词
Critical temperature; Superconductivity; Multivariate adaptive regression splines (MARS); Whale optimization algorithm (WOA)
资金
- European Funds (FEDER) [PGC2018-098459-B-I00, FC-GRUPIN-IDI/2018/000221]
This study developed a predictive model based on a hybrid WOA/MARS approach to successfully estimate the critical temperature of a superconductor. Results showed that the WOA/MARS model outperformed the Ridge, Lasso, and Elastic-net regression models in predicting the critical temperature.
This study builds a predictive model capable of estimating the critical temperature of a superconductor from experimentally determined physico-chemical properties of the material (input variables): features extracted from the thermal conductivity, atomic radius, valence, electron affinity and atomic mass. This original model is built using a novel hybrid algorithm relied on the multivariate adaptive regression splines (MARS) technique in combination with a nature-inspired meta-heuristic optimization algorithm termed the whale optimization algorithm (WOA) that mimics the social behavior of humpback whales. Additionally, the Ridge, Lasso and Elastic-net regression models were fitted to the same experimental data for comparison purposes. The results of the current investigation indicate that the critical temperature of a superconductor can be successfully predicted using this proposed hybrid WOA/MARS-based model. Furthermore, the results obtained with the Ridge, Lasso and Elastic-net regression models are clearly worse than those obtained with the WOA/MARS-based model.
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