TY  - JOUR
T1  - Hybrid Support Vector Regression and Genetic Algorithm Model for Tuning
Magnetic Ordering Temperature of Manganite Refrigerant
AU - Alqahtani, Abdullah AU - Owolabi, Taoreed O. AU - Akanded, Kabiru O. AU - Olatunji, Sunday O. AU - Aldhafferi, Nahier 
JO  - Research Journal of Applied Sciences
VL  - 13
IS  - 1
SP  - 87
EP  - 93
PY  - 2018
DA  - 2001/08/19
SN  - 1815-932x
DO  - rjasci.2018.87.93
UR  - https://makhillpublications.co/view-article.php?doi=rjasci.2018.87.93
KW  - Manganite based material
KW  -support vector regression
KW  -Genetic Algorithm
KW  -crystal lattice parameters and magnetic ordering temperature
KW  -technology
KW  -ozone
AB  - Manganite based materials have several unique properties which include low cost, environmental
friendliness and huge magnetocaloric effect which make them suitable as magnetic refrigerant in Magnetic
Refrigeration Ttechnology (MRT). However, effective utilization and deployment of this technology requires
manganite refrigerants with magnetic ordering Temperature (T ) around ambient temperature. In order to tune C
the T of manganite based materials to the desired ambient value and circumvent experimental stress therein, C
a hybrid of Support Vector Regression (SVR) Algorithm and Genetic Algorithm (GA) is proposed using the
crystal lattice structural parameter of manganite based materials as the descriptors to the models. The
generalization and predictive strength of the proposed hybrid GA-SVR Model is compared with the existing
Gravitational Search Algorithm based model (GSA-SVR) on the basis of error reduction as well as computational
complexity. The proposed hybrid GA-SVR Model outperforms the existing GSA-SVR Model with 8% and 2%
performance improvement on the basis of the coefficient of correlation and root mean square error, respectively.
Comparison of the proposed hybrid GA-SVR Model with the existing Manual Search based Support Vector
Regression (MS-SVR) shows that the developed hybrid GA-SVR model outperforms MS-SVR Model with
percentage improvement of 4.7% on the basis of correlation coefficient. Better performance demonstrated by
GA-SVR Model coupled with its less computational time, strengthens its potential in enhancing room
temperature magnetic refrigeration and promoting environmental friendly technology over ozone-depleting
refrigeration technology.
ER  -