Space Optimization Design of Building Environment Based on Particle Swarm Optimization Neural Network

Ling Xia

2025

Abstract

Space optimization design plays an important role in the space of intelligent building environment, but there is a problem of inaccurate optimization positioning. Traditional deep learning cannot solve the spatial optimization problem in the space of intelligent building environment, and the effect is not satisfactory. With the continuous advancement of artificial intelligence technology, its application in architectural design and management is becoming more and more extensive. Especially in the field of spatial optimization of the built environment, the combination of particle swarm optimization (PSO) algorithms and neural network technology is gradually changing the way we design and use built spaces. This intelligent approach not only improves energy efficiency and functionality, but also leads to a more comfortable and healthy environment for occupants and occupants.

Paper Citation

in Harvard Style

Xia L. (2025). Space Optimization Design of Building Environment Based on Particle Swarm Optimization Neural Network. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 194-199. DOI: 10.5220/0013538200004664

in Bibtex Style

@conference{incoft25,
author={Ling Xia},
title={Space Optimization Design of Building Environment Based on Particle Swarm Optimization Neural Network},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT},
year={2025},
pages={194-199},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013538200004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT
TI - Space Optimization Design of Building Environment Based on Particle Swarm Optimization Neural Network
SN - 978-989-758-763-4
AU - Xia L.
PY - 2025
SP - 194
EP - 199
DO - 10.5220/0013538200004664
PB - SciTePress