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<p>With the increasing global attention to sustainable development, intelligent optimization methods for interactive public space lighting environments are occupying an increasingly important position in the field of architectural design. However, it also faces challenges in terms of technology, capital investment, and cost control in its application. To address the issues of low efficiency, poor population diversity, and limited interactive experiences in traditional lighting environment optimization methods, this study proposes a technique integrating Non-Dominated Sorting Genetic Algorithm II with virtual reality and augmented reality technologies. By identifying approximate optimal solutions for influencing factors such as energy consumption control, lighting quality, and user experience, the method optimizes public space lighting schemes and ultimately constructs a multi-objective optimization model for lighting environments. Multiple models are selected for the evaluation and comparison of performance indicators in the experiment. The performance indicators of the research model are superior to other models, with inverse generation distance, super volume, spacing, and Spread being 0.025, 0.90, 0.012, and 0.98. Additionally, this model demonstrates outstanding advantages in feature extraction and optimization accuracy, reducing energy consumption by 15.1% compared to most multi-objective particle swarm optimization models, with pedestrian comfort scores reaching as high as 9.5 points. The experiment shows that the proposed multi-objective optimization model for light environment has significant accuracy and efficiency in light environment optimization, and has great potential and practicality.</p>

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Multi-objective intelligent optimization of public space light environment based on improved NSGA-II and VR fusion technology

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月5日

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  • Fei Xu

摘要

With the increasing global attention to sustainable development, intelligent optimization methods for interactive public space lighting environments are occupying an increasingly important position in the field of architectural design. However, it also faces challenges in terms of technology, capital investment, and cost control in its application. To address the issues of low efficiency, poor population diversity, and limited interactive experiences in traditional lighting environment optimization methods, this study proposes a technique integrating Non-Dominated Sorting Genetic Algorithm II with virtual reality and augmented reality technologies. By identifying approximate optimal solutions for influencing factors such as energy consumption control, lighting quality, and user experience, the method optimizes public space lighting schemes and ultimately constructs a multi-objective optimization model for lighting environments. Multiple models are selected for the evaluation and comparison of performance indicators in the experiment. The performance indicators of the research model are superior to other models, with inverse generation distance, super volume, spacing, and Spread being 0.025, 0.90, 0.012, and 0.98. Additionally, this model demonstrates outstanding advantages in feature extraction and optimization accuracy, reducing energy consumption by 15.1% compared to most multi-objective particle swarm optimization models, with pedestrian comfort scores reaching as high as 9.5 points. The experiment shows that the proposed multi-objective optimization model for light environment has significant accuracy and efficiency in light environment optimization, and has great potential and practicality.