## 4 Conclusions  
In the context of global energy crisis, building performance optimization has been widely concerned. This paper reviewed the existing building multiobjective optimization literatures and demonstrated the limitations. The BPS based multiobjective optimization was time-consuming, inefficient and it got worse when faced with many objectives. In the existing ANN based multiobjective optimization research, the computer coding ability on non-architectural platforms was required, which was not friendly to architects. In addition, large-scale public buildings, which are energy consuming, have rarely been studied. To solve the limitations, this paper proposed an architect oriented ANN based many-objective optimization method. The details of the method and the optimization design model were presented and a large-scale public building was selected as the reference building. The optimization process obtained 176 non-dominated solutions, the average EUI value of them was 199.9 kWh/m\({}^{2}\), the average sDA value was 87.2%, the average UDI value was 58.4% and the average BEC value was \(\chi\)2.21\(\times\)10\({}^{6}\) ($3.17\(\times\)10\({}^{5}\)). The energy, daylighting and envelope cost performance of the building could be improved significantly by adopting one of the selected relative optimal solutions. The conclusions are summarized as follows:  
1. The ANN prediction models were coupled in the optimization process, by which the optimization time was greatly shortened. The inefficiency problem of the traditional BPS based multiobjective optimization method was solved in this way.
2. LHS was used to obtain design samples in design space for performance simulation, in which way a good representation of design space could be achieved by a small sample number, so that the simulation times could be largely reduced.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Optimization Objectives** & **EUI** & **sDA** & **UDI** & \multicolumn{2}{c}{**BEC**} \\ \hline
**Unit** & **kWh/m\({}^{2}\)** & **\%** & **\%** & **\%** & **\$** \\ \hline Original Design & 203.1 & 79.1 & 55.4 & 2.10\(\times\)10\({}^{6}\) & 3.01\(\times\)10\({}^{5}\) \\ \hline \multirow{3}{*}{Relative Optimal Solutions} & 196.9 & 89.3 & 59.7 & 1.98\(\times\)10\({}^{6}\) & 2.84\(\times\)10\({}^{5}\) \\ \cline{2-6}  & 198.0 & 90.1 & 59.8 & 1.91\(\times\)10\({}^{6}\) & 2.74\(\times\)10\({}^{5}\) \\ \cline{2-6}  & 196.4 & 87.2 & 60.1 & 1.89\(\times\)10\({}^{6}\) & 2.71\(\times\)10\({}^{5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Optimization objectives of the original design and the relative optimal solutions.  
* Compared with the existing ANN based multiobjective optimization, the proposed method coupled an automated optimization process with the ANN modeling process, through which architects without experience in ANN modeling can obtain high-precision ANN prediction models.
* In this study, all the modules of the optimization design model were integrated in the Rhinoceros + Grasshopper platform, and various software and plug-ins were integrated in the proposed model. Different modules were coupled with each other, so manual data conversion was not required, which enhanced the operability of the whole process to be an architect-friendly workflow.  
[PERSON], [PERSON] and [PERSON] conceived the research, developed the model and analyzed the data. All the authors were engaged in final manuscript preparation and writing. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Natural Science Foundation of China (grant number 51708149); National Natural Science Foundation of China (grant number 51938003); and the China Postdoctoral Science Foundation Funded Project (grant number 2017M621276) and the Heilongjiang Postdoctoral Science Foundation (grant number LBH-Z17076).  
The authors declare no conflicts of interest.