This paper proposes a framework for multi-objective optimization of indoor daylighting and thermal comfort in natatoriums with retractable roofs, using genetic algorithms and building performance simulation. The goal is to balance daylight illumination and thermal comfort under different roof opening states. Efficiency of computational fluid dynamics simulation is improved by a multi-objective optimization framework based on non-dominated sorting genetic algorithms, integrating a generative adversarial network to learn the environmental map. The study analyses the impact of different roof opening ratios on environmental objectives and establishes regression equations to support optimization and accurate decision-making. In the optimal opening state, compared to a fully closed roof, the daylight factor and natural airflow velocity increase by up to 3.9 and 5.3 times, respectively, while the universal thermal climate index decreases by 1.0 °C. The introduction of a generative adversarial network proxy model significantly improves computational efficiency, accelerating natural airflow velocity calculation by 70–100 times and reducing simulation time by 98.6 %. This study expands predictive models from the urban scale to the building scale, enhancing simulation efficiency and promoting the application of deep learning models and genetic algorithms in climate-responsive building environment assessment and prediction.

中文翻译：

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基于生成对抗网络和遗传算法的可伸缩屋顶游泳馆日光热环境优化框架


本文提出了一个利用遗传算法和建筑性能模拟对具有可伸缩屋顶的游泳馆室内采光和热舒适度进行多目标优化的框架。目标是平衡不同屋顶打开状态下的日光照明和热舒适度。通过基于非支配排序遗传算法的多目标优化框架，集成生成对抗网络来学习环境图，提高了计算流体动力学模拟的效率。研究分析了不同屋顶开口率对环境目标的影响，并建立回归方程以支持优化和准确决策。在最佳开启状态下，与全封闭屋顶相比，日光系数和自然气流速度分别增加高达3.9和5.3倍，而通用热气候指数则降低1.0℃。生成对抗网络代理模型的引入显着提高了计算效率，将自然气流速度计算速度加快了70-100倍，并将模拟时间缩短了98.6%。该研究将预测模型从城市尺度扩展到建筑尺度，提高了模拟效率，推动了深度学习模型和遗传算法在气候响应型建筑环境评估和预测中的应用。