## 3 Results  
### Results of Microclimate Optimization at the Block Level  
In this study, 972 generations were obtained in total, of which 601 generations were valid (the optimization target value was not 0). After removal of invalid data, the change line of the optimization target value was drawn, as shown in Figure 5. The comfort index of the entire simulated area increased from 0 to 180 generations and tended to be stable with the calculation. Notably, the large graph fluctuations are determined by characteristics of the genetic algorithm itself to prevent the continuous selection of similar \"high-quality genes,\" resulting in \"inbreeding.\" Occasionally, the algorithm selected genes that differed greatly from high-quality genes to increase diversity and prevent premature convergence. \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline \multicolumn{2}{c}{**Block settings**} \\ \hline Size of basic block & 100 m \(\times\) 100 m \\ Number of blocks & 9 \\ \hline Building from & As shown in Figure 2 \\ \hline \multicolumn{2}{c}{**Optimization settings**} \\ \hline Simulation period & 8:00 a.m.–8:00 p.m., Feb 15 \\ Wind direction & 30\({}^{\circ}\) east to the north \\ Initial wind speed & 4 m/s \\ \& Maximum wind speed threshold & 4 m/s \\ Density interval & 0.25–0.45 \\ Plot ratio interval & 3–5.5 \\ Comfort interval & \(-\)17–20 \({}^{\circ}\)C \\ Street width & 20, 30, and 40 m \\ \hline \multicolumn{2}{c}{**Optimization assessment settings**} \\ \hline \multicolumn{2}{c}{Building density} \\ \multicolumn{2}{c}{Plot ratio} \\ Factors of urban morphology & Sky view factor (SVF) \\ \multicolumn{2}{c}{Building enclosure} \\ \multicolumn{2}{c}{Variation of building heights} \\ \multicolumn{2}{c}{Mean radiant temperature (MRT)} \\ Factors of urban microclimate & Universal thermal comfort index (UTCI) \\ \multicolumn{2}{c}{Average wind speed} \\ \multicolumn{2}{c}{Unacceptable wind percentage} \\ \hline \end{tabular}
\end{table} 
Table 2: Parameter settings and boundary conditions during optimization. Figure 5: Change trends in comfort ratios generated by Galapagos genetic algorithm at the block level. To verify effectiveness of the optimization results, we selected two urban designs for the best and worst acceptable UTCI percentage along with the comfort level. The data in Figure 6 show that, through program optimization, the overall block comfort ratio increased from 73.6% in the poorest case to 87.7%, a growth of about 19%. This demonstrates that optimization can effectively improve the microclimate comfort performance of the whole block combination by searching for the optimal combination of urban morphology. In addition, results through optimization with higher than 85% acceptable UTCI percentage are used to check the building form characteristics that achieved good results. Figure 7 shows the building form distribution, illustrating the percentage of building forms in each block. The whole urban block was divided into nine individual blocks, indexed with columns A, B, C, and rows 1, 2, 3. According to Figure 7, building forms 1 and 2 were the top two types (A1, A2, B1, B2, C2, and C3 block area); building form 3 occurred at a high frequency (A3, B3, and C1); building form 4 did not appear in any high-frequency location of the block, and building form 5 (open space) did not appear in any generation. Multistory blocks 1 and 2 were chosen more frequently than high-rise blocks 3 and 4 and open space 5. Open space significantly reduces the building density of the whole block. Hence, this form must be strictly controlled in the early stage. Based on micro-climate effects, high-rise forms and large-area open spaces will result in surrounding unfavorable wind environments, especially with high-rise form 4 compared to high-rise form 3. In the distribution locations, the high-frequency form at A1 was building form 2 with the highest building enclosure degree, which blocked cold air to the maximum extent and provided protection for the high-level flank of the B1 location.