  The significance of heating load (HL) accurate approximation is the primary
motivation of this research to distinguish the most efficient predictive model
among several neural-metaheuristic models. The proposed models are through
synthesizing multi-layer perceptron network (MLP) with ant lion optimization
(ALO), biogeography-based optimization (BBO), dragonfly algorithm (DA),
evolutionary strategy (ES), invasive weed optimization (IWO), and league
champion optimization (LCA) hybrid algorithms. Each ensemble is optimized in
terms of the operating population. Accordingly, the ALO-MLP, BBO-MLP, DA-MLP,
ES-MLP, IWO-MLP, and LCA-MLP presented their best performance for population
sizes of 350, 400, 200, 500, 50, and 300, respectively. The comparison was
carried out by implementing a ranking system. Based on the obtained overall
scores (OSs), the BBO (OS = 36) featured as the most capable optimization
technique, followed by ALO (OS = 27) and ES (OS = 20). Due to the efficient
performance of these algorithms, the corresponding MLPs can be promising
substitutes for traditional methods used for HL analysis.
