DOI: https://doi.org/10.61435/ijred.2024.60308

Decomposition based multi-objective evolutionary algorithm for energy-saving design of homestay buildings

1School of Arts, Zhengzhou Technology and Business University, Zhengzhou, 451400, China

2Art and Design, Zhengzhou Electronic & Information Engineering School, Zhengzhou, 450007, China

Received: 16 May 2024; Revised: 18 Jul 2024; Accepted: 12 Aug 2024; Available online: 27 Aug 2024; Published: 1 Sep 2024.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2024 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract

To improve the prediction accuracy of energy-saving design for homestay buildings, a multi-objective optimization model is studied. A model of multi-objective optimization algorithm for energy efficiency design of home stay buildings based on decomposition multi-objective evolutionary algorithm is proposed. Decomposition based multi-objective evolutionary algorithm is selected. To select the preliminary algorithm for achieving energy-saving design of homestay buildings, it divides the objectives into algorithm determination and model construction and uses multi-objective optimization algorithms to solve the proposed optimization model. The validation results show that the minimum discomfort time calculated using the non-dominated sorting genetic algorithm is 555.30 and the energy consumption is 7.68, while the minimum discomfort time calculated using the non-dominated sorting genetic algorithm method is 896 and the energy consumption is 8.92. With alternative model, the speed of multi-objective Evolutionary algorithm is the fastest, reaching 6105.44 seconds, which is 68.80% lower than the proposed method. With the help of substitutes, the computational speed of the multi-objective particle swarm optimization algorithm has been greatly improved. Its computational speed has reached 1217.231 seconds, while the fastest multi-objective particle swarm optimization algorithm among the four comparison methods is only 3868.591 seconds. Although the individual improvement is not significant, the overall optimization is still considerable and has strategic foresight in the decision-making plan of decision-makers.

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Keywords: MOEAD;Homestay buildings; Energy-saving design; Sa-MOOSO; Multi-objective optimization

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Section: Original Research Article
Language : EN
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