Application of Particle Swarm Algorithm in Architectural Design Teaching System Under VR
- DOI
- 10.2991/978-94-6463-040-4_227How to use a DOI?
- Keywords
- VR; Particle swarm optimization; Architectural Design Teaching
- Abstract
As a new technology, VR is changing people's study, life, work and entertainment, and has been widely used in various fields. Virtual reality, numerical control processing, artificial intelligence and other digital technologies are increasingly entering the architectural design classroom. Their changes to architectural teaching highlight the necessity of establishing a digital architectural design teaching system in architectural teaching. In order to solve the dilemma in this teaching practice, a very intuitive teaching method, computer simulation teaching method, is explored, and computer demonstration is used to cooperate with classroom teaching to give students a relatively acceptable teaching experience. The principle and workflow of particle swarm optimization algorithm are introduced. This paper will analyze the advantages of VR in the field of architectural design teaching in the information age, and discuss the application of VR in the field of architectural design teaching.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Qiao Wang AU - Dante Caseldo PY - 2022 DA - 2022/12/27 TI - Application of Particle Swarm Algorithm in Architectural Design Teaching System Under VR BT - Proceedings of the 2022 3rd International Conference on Artificial Intelligence and Education (IC-ICAIE 2022) PB - Atlantis Press SP - 1517 EP - 1522 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-040-4_227 DO - 10.2991/978-94-6463-040-4_227 ID - Wang2022 ER -