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

TL;DR

This research compares various neural-metaheuristic hybrid models for accurately predicting heating load in residential buildings, finding BBO-MLP as the most effective among tested algorithms.

Contribution

It introduces a novel comparison of multiple hybrid neural network models optimized by different metaheuristics for heating load prediction.

Findings

BBO-MLP achieved the highest overall score of 36.

A comparison ranking identified BBO, ALO, and ES as top performers.

Hybrid models outperform traditional heating load prediction methods.

Abstract

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.