## 1 Introduction
optimized the daylighting and building energy consumption by varying building forms and WWR parameters [15]. In 2017, [PERSON] et al. optimized the minimal energy use for heating and lighting, minimal summer discomfort time, and maximal Useful Daylight Illuminance (UDI) of typical classrooms in the cold regions of China through optimization process [16]. In 2018, [PERSON] et al. studied a residential building to find the best building parameters such as WWR, construction material, glass material and shading device configurations, which lead to the optimum performance in daylighting and energy performance [17]. The current trend indicates that building performance optimization has been increasingly deployed both in building academia research and practice [18]. However, the above studies usually optimized two or three building performances, and the number of objective functions was rarely larger than three. The limitation lies in the BPS based objective evaluation model, which is time-consuming [19]. As the number of objectives grows, the complexity and the time required for searching solutions will increase exponentially. Multiobjective problems with four or more objectives are often referred to as many-objective problems [20], and practical projects especially large-scale public building design projects often involves such problems because of the complex nature of the building optimization problems. To solve the many-objective optimization bottleneck problems in building design, many researchers have explored methods of coupling surrogate models with optimization tools to improve the efficiency of searching techniques for large-scale building optimization problems [8]. Artificial Neural Networks (ANNs) are effective tools that can solve intricate nonlinear problems, especially where classical mathematical modeling processes fail to succeed [21],and are the most commonly used surrogate models for objective evaluation in building performance optimization process. In 2013, [PERSON] et al. coupled an ANN and non-dominated sorting genetic algorithm II (NSGA-II) to optimize the annual energy consumption and the summer comfort degree of a dwelling in two French climates, and selected different thermal properties of the building envelope as decision variables [22]. In 2015, [PERSON] et al. used back-propagation (BP) neural network optimized by genetic algorithm (GA) to evaluate the energy consumption and indoor thermal comfort during the optimization process of typical residential buildings [23]. In 2016, [PERSON] et al. combined GA with an ANN to explore optimum building envelope design with respect to energy use and life cycle contribution to the impacts on the environment in a low-rise office building [24]. In 2018, using the ANN model and NSGA-II algorithm, [PERSON] et al. optimized the passive design of newly built residential buildings in China for improving indoor thermal comfort while reducing building energy demand [25]. In 2019, [PERSON] et al. optimized the window types and the eave shape of a tourist center in China to improve building energy efficiency and indoor thermal comfort, and developed an ANN model for building performance prediction [26]. [PERSON] et al. tested the performances of three building optimization strategies, including two BPS based methods and an ANN based, the ANN based method was proved to have the shortest execution time [27]. Even so, there are still few researches on building many-objective optimization. When faced with many-objective problems, the most widely adopted building multiobjective optimization algorithm, NSGA-II, shows a severe deterioration on the convergence property [20]. In addition, most of the researchers developed ANN models in MATLAB, which is a non-architectural platform and requires a computer coding ability. The dilemma is that a traditionally trained architect's design decisions have a great impact on building performance, but they are not familiar with optimization algorithms and complex programs, let alone computer coding [28].