Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 11 January 2021 / Approved: 12 January 2021 / Online: 12 January 2021 (14:46:29 CET)

A peer-reviewed article of this Preprint also exists.

Moayedi, H.; Mosavi, A. 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. Sustainability 2021, 13, 3198. Moayedi, H.; Mosavi, A. 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. Sustainability 2021, 13, 3198.

Journal reference: Sustainability 2021, 13, 3198
DOI: 10.3390/su13063198

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.

Subject Areas

energy-efficient building; heating load; neural computing; biogeography-based optimization

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