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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Engineering letters Ročník 33; číslo 10; s. 4098
Hlavný autor: Yuan, Yousheng
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Hong Kong International Association of Engineers 01.10.2025
Predmet:
ISSN:1816-093X, 1816-0948
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1816-093X
1816-0948