## 1 Introduction  
Cities and energy play a key role in solving the environmental crisis [1], and building energy efficiency has received a widespread interest. More than 1/3 of the total global energy consumption can be attributed to the building and construction sectors [2]. According to the research report released by CABEE (China Association of Building Energy Efficiency) in 2018, building energy consumption is 20.6% of the country's total energy consumption, statistical data also shows that public buildings and urban residential buildings account for the two highest proportions of building energy consumption, each with a percentage of 38% [3]. Besides, the energy consumption per unit floor area in public buildings is more than twice as much as that in residential buildings [4]. Hence, energy efficiency of buildings especially public buildings needs to be optimized. To reach the best possible solutions that will ensure the maximization of the building energy efficiency and satisfy the needs of the building's final user/occupant/owner simultaneously [5], designershave to make tradeoff decisions among environmental, energy efficiency, financial and occupant comfort performance. In the early stage, architects relied on previous experience and building performance simulation to find the best possible building design solutions. The trial-and-error approach is too time-consuming to be practical [6]. Thus, some researchers tried to introduce optimization techniques, which can greatly improve the design efficiency and support designers to find the optimal design [7]. In a conventional building performance optimization study, an automated process can be achieved by the coupling between building simulation tools and an optimization engine [8]. This method is known as Building Performance Simulation (BPS) based optimization. As multiple performance objectives involved in building design usually have conflicts with each other, the improvement of one objective often leads to the decline of other objectives. Multiobjective optimization is introduced and widely adopted as it can simultaneously improve more than one building performance objectives. Through BPS based multiobjective optimization, designers can explore complex performance trade-offs in building design, consider the effect of design changes on multiple performance objectives as well as search for optimal solutions in design space [9]. Previous studies have used the BPS based multiobjective optimization method during the building design process to optimize the performance related to building energy efficiency, environmental impact, daylighting, thermal comfort, cost, etc. In 2010, [PERSON] and [PERSON] adopted a genetic algorithm to minimize energy use and life-cycle cost of residential buildings, and investigated various building forms as well as building envelope parameters [10]. In 2012, [PERSON] et al. proposed a multiobjective optimization model based on the harmony search algorithm and optimized the envelope parameters of a residential building to minimize the life cycle cost and carbon dioxide equivalent emissions [11]. In 2013, [PERSON] et al. selected the window to wall ratio (WWR) and the window type as decision variables, optimized the heating load, cooling load and daylighting performance of a dormitory room [12]. In 2015, [PERSON] et al. optimized the number, position, shape and type of windows and the thickness of the masonry walls to minimize the heating, cooling and lighting energy of an open space office building [13]. In the same year, [PERSON] et al. developed a building information modeling (BIM)-based performance optimization (BPOpt) framework and demonstrated the use of BPOpt in minimizing the energy consumption while maximizing the appropriate daylighting level for a residential building, the decision variables were seven parameters related to windows and curtain panels [14]. In 2016, using idealized mass models of commercial buildings, [PERSON] et al.