Development of Hybrid Gravitational Search Algorithm Based Support Vector Regression for Predicting Magnetic Ordering Temperature of Manganite using Ionic Radii Descriptors

Abstract

Unique characteristic features of Magnetic Refrigeration Technology (MRT) which include environmental friendliness and better efficiency have promoted the chance of replacing the conventional Compression Gas System of Refrigeration (CGSR). One of the main challenges associated with practical implementation of MRT is the tuning of its magnetic ordering temperature (TC) to ambient value. The present work utilizes the ionic radii as potential descriptors to develop hybrid Support Vector Regression (SVR) and Gravitational Search Algorithms (GSA) based model for estimating magnetic ordering temperature. The outcomes of the proposed method agreed closely with the experimental values. With the outstanding performance of the proposed hybrid SVR-GSA model, the magnetic ordering temperature of manganite (which serves as the refrigerant) can be easily tuned to the ambient value while dependency on ozone-depleting CGSR can be minimized.