Green Building Design Optimization Based on Multi-Objective Particle Swarm Algorithm

With the development of society and the growth of human demands, energy consumption is also continuously increasing. Green building energy-saving design is currently an important component in the field of energy consumption. It is also one of the current challenges that need to be addressed. To opti...

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Bibliographic Details
Published in:Engineering letters Vol. 33; no. 10; p. 4098
Main Author: Yuan, Yousheng
Format: Journal Article
Language:English
Published: Hong Kong International Association of Engineers 01.10.2025
Subjects:
Adaptive algorithms
Building design
Clean energy
Design optimization
Energy conservation
Energy consumption
Green buildings
Multiple objective analysis
Optimization algorithms
Optimization models
Particle swarm optimization
ISSN:1816-093X, 1816-0948
Online Access:Get full text
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Summary:With the development of society and the growth of human demands, energy consumption is also continuously increasing. Green building energy-saving design is currently an important component in the field of energy consumption. It is also one of the current challenges that need to be addressed. To optimize the design of green buildings and reduce energy consumption while ensuring building comfort, a multi-objective green building energy-saving optimization model is constructed. An improved multi-objective back-bone particle swarm optimization algorithm based on adaptive disturbance factors is designed. To reduce the running time, a decomposition-based proxy model assisted multi-objective particle swarm optimization algorithm is designed. A new sample selection strategy guided by dual reserve sets is also designed. According to the experimental results, in single room and three bedroom buildings, the average hypervolume measurement values were 29311 and 49504, respectively. The average hypervolume measurement values of the proxy model were 21153 and 40230, respectively. The designed algorithm has good performance, which can provide technical support for the optimization design of green buildings.
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ISSN:1816-093X
1816-0948