Nature-inspired metaheuristic multivariate adaptive regression splines for predicting refrigeration system performance

This study aims to build an artificial intelligence (AI)-based inference model to predict the coefficient of performance (COP) for refrigeration equipment under various R404A refrigerant conditions. The proposed model, the evolutionary multivariate adaptive regression splines (EMARS), is a hybrid of the multivariate adaptive regression splines (MARS) and the artificial bee colony (ABC). In the EMARS, the MARS primarily addresses the learning and curve fitting and the ABC carries out optimization to determine the fittest parameter settings with minimal prediction error. A tenfold cross-validation method was used to compare the performance of the EMARS against four other AI techniques, including the back-propagation neural network, classification and regression tree, genetic programming, and support vector machine. An analysis of comparison results supports EMARS as the best model for predicting the COP, with an MAPE value \(<\)1%. In addition to performing significantly better than the four benchmark models, EMARS is unique in being able to: operate automatically without human intervention or domain knowledge; explore the approximate function of the input–output relationship; and identify the relative importance of various factors of influence automatically.