## 6 Conclusions  
The above study and the analysis of the results lead to the following conclusions:  
* In some common scenarios, the method in this paper can quickly provide an approximate Pareto Frontier. However, in the face of scenarios where the response values change rapidly, are locally non-differentiable, or are intermittent (surrogate models generate large gradients, and model errors make it challenging to optimize correctly), \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **CFD-Based** & **POD Method** &
\begin{tabular}{c} **Surrogate-Based** \\ **(This Paper)** \\ \end{tabular} \\ \hline Target number (achievable) & multiple & multiple & multiple \\ Target number & multiple & single & double \\ (current study) & small & large & medium \\ Initial sample size & small & uniform/orthogonal & Latin hypercube sampling \\ Sampling method & random & experimental design & technique \\ Randomness & existent & non-existent & existent \\ Continuity assumption & non-existent & existent & existent \\ Prediction process & non-existent & interpolation & interpolation \\ Prediction method & non-existent & spline/polynomial/ & Kriging \\  & radial basis function & existent \\ Gradient dependence & non-existent & existent & existent \\ Database update & existent & usually non-existent & existent \\ Subgeneration generation tool & three kinds of operators & usually non-existent & infill criteria and NSGA-II \\ Number of new individuals & usually equal to the initial & usually non-existent & few \\  & sample size & & \\ Validation times & many & one or few & many \\ Effect of outliers & non-existent & existent & existent \\ Costs & higher & medium & lower \\ \hline \hline \end{tabular}
\end{table} 
Table 6: A summary of the three methods (with multiple parameters). the optimization may stall, because this paper assumes that the response values should be continuous and smooth, and then the Kriging model is used. Therefore, the method is more suitable for scenarios where the response values vary gently. In optimization based on 3D steady-state simulation, the use of discrete optimization with an appropriate discrete length can alleviate the above problems, speed up the optimization (sample size reduced by approximately 60%), and maintain accuracy. * The method in this paper, as a development of meta-heuristic optimization approaches, also has global search capability when a sufficient number of initial samples are provided. The difference is that the method generates a subgeneration with few individuals based on the prediction of the surrogate model, which improves the utilization of the samples, makes the subgeneration more likely to be generated in the right direction, greatly reduces the total number of samples, and reduces the computational cost. However, compared with the meta-heuristic optimization approaches that use a selection operator, crossover operator, and mutation operator to generate a subgeneration with many individuals directly, the diversity of subgeneration and the adaptability of the method decrease. * After dividing the dual-aisle cabin into two zones, the air supply parameters were optimized using this paper's method. A total of 118 samples (cases) were calculated to obtain an approximate Pareto Frontier. For such a five-parameter optimization process, thousands of samples may be required based on meta-heuristic optimization approaches. When using the POD method, if each parameter is divided into four intervals, uniform sampling requires 3125 cases to compose the initial database. The generated Pareto set suggests that an airflow organization with a left-right symmetric circulation structure may be optimal. The volumes of the two controlled areas meeting thermal comfort conditions are 88% and 82%, respectively. Y.G. : Writing--review and editing, Writing--original draft, Visualization, Validation, Software, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Y.W.