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The significance of heating load (HL) accurate approximation is the primarymotivation of this research to distinguish the most efficient predictive modelamong several neural-metaheuristic models. The proposed models are throughsynthesizing 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 leaguechampion optimization (LCA) hybrid algorithms. Each ensemble is optimized interms of the operating population. Accordingly, the ALO-MLP, BBO-MLP, DA-MLP,ES-MLP, IWO-MLP, and LCA-MLP presented their best performance for populationsizes of 350, 400, 200, 500, 50, and 300, respectively. The comparison wascarried out by implementing a ranking system. Based on the obtained overallscores (OSs), the BBO (OS = 36) featured as the most capable optimizationtechnique, followed by ALO (OS = 27) and ES (OS = 20). Due to the efficientperformance of these algorithms, the corresponding MLPs can be promisingsubstitutes for traditional methods used for HL analysis.

Synthesizing multi-layer perceptron network with ant lion,  biogeography-based dragonfly algorithm evolutionary strategy invasive weed  and league champion optimization hybrid algorithms in predicting heating load  in residential buildings