Year: 2025
Type: article
Abstract: ABSTRACT Multiplayer virtual reality (VR) games represent the increasing future direction of VR game development. However, currently it still falls short in terms of the fluidity of social interaction, response speed, and sense of immersion, which affects players' engagement and satisfaction. This paper proposes an optimisation design for multiplayer VR game systems that integrates BP neural networks based on an intelligent computing framework. By applying the fast convergence of traditional BP algorithms and enhancing it with genetic algorithms to improve global search capabilities and avoid local optima, ensuring more accurate and efficient neural network training for enhanced VR gaming experiences. This paper compares the performance of traditional neural networks and evolutionary neural networks through extensive simulation and testifies to the engagement experience of players through quantitative analysis. The results show that evolutionary neural networks outperform traditional neural networks in system performance, such as severe latency and technical lagging. The paper also finds that technological preparedness significantly affects behaviour engagement through embodied social presence, emotional and cognitive engagement. Based on these findings, this paper suggests strategies to optimise the user experience of multiplayer VR games by improving game technology quality, enriching content, maintaining continuous communication with players, and establishing reasonable incentive mechanisms.
Source: IET Communications
DOI: 10.1049/cmu2.70056
Authors: Ling Yang, Daibo Xiao
Institutions: Guangzhou Xinhua University, City University of Macau
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,540
OpenAlex ID: W4411345933