## 4 Experimental Setup and Result Analysis
### Results and Discussion  
Generally speaking, a single-objective optimization problem means that there is only one optimization objective function, and when there are two or more optimization objective functions, it becomes a multi-objective optimization problem. In this research, we deal with multi-objective optimization problems because our objectives are to save energy and provide life comfort in intelligent building scenarios. Figure 5 shows the result of the proposed model for 30 iterations. As shown in Figure 5, the heat transfer coefficients of southeast, northwest, and facing the exterior wall are 0.66, 0.8, 6.1, and 3.6, respectively. Therefore, it can provide a reference for the selection of exterior wall insulation materials. At this time, the power consumption is 9.2, the absolute value of the predicted average number of predicted mean vote (PMV) is 1.9, and the cost of external wall insulation materials and windows is 452,600 yuan. Because it is difficult to achieve the best state of building wasting resources, cost, and thermal comfort in practical projects, a niche genetic algorithm is used to provide some reference for intelligent building design so that the three can reach a relatively good state. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Function Type** & **Function** \\ \hline \multirow{3}{*}{Create a population} & \multicolumn{2}{c}{Crtb\(\underline{\text{p}}\)} & Create basis vector \\ \cline{2-3}  & \multicolumn{2}{c}{Crt\(\underline{\text{p}}\)} & Create any discrete random population. \\ \cline{2-3}  & \multicolumn{2}{c}{Crt\(\underline{\text{p}}\)} & Create a real-valued initial population \\ \hline \multirow{3}{*}{Fitness calculation} & \multicolumn{2}{c}{Rws} & Roulette wheel selection \\ \cline{2-3}  & \multicolumn{2}{c}{Select} & Advanced selection process \\ \cline{2-3}  & \multicolumn{2}{c}{Sus} & Random sampling \\ \hline \multirow{3}{*}{Select function} & Recdis & Discrete variation \\ \cline{2-3}  & \multicolumn{2}{c}{Recint} & Linear recombination \\ \cline{2-3}  & \multicolumn{2}{c}{Reclin} & Real value variation \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of common functions of niche genetic algorithm. Figure 5: Change in the mean value of objective function during 30 iterations. Tables 2 and 3 summarize the heat transfer coefficient and various parameters related to power consumption, respectively. From the simulation results in Figure 6, it can be seen that the multi-objective optimization theory and the principle of genetic algorithm choose the heat transfer coefficient of the building exterior wall and window as the optimization variables, so as to achieve the goals of lower building waste of resources, enclosure cost, and better thermal comfort. Table 3 shows the cost and value of different parameters including general power consumption, PMV value, and cost of insulation materials. When a niche genetic algorithm is used to solve optimization problems, the algorithm does not limit the number of identical individuals or similar individuals. However, when niche technology is introduced into genetic algorithms, the number of them will be limited in order to produce more kinds of different optimal solutions. For an individual, how many kinds and how many similar individuals exist in its vicinity can be measured, and this measurement is called a niche number. Table 4 shows the evaluation data of different zonal areas within the intelligent building environment such as the living room, bedroom, bathroom, kitchen, and offices. We \begin{table}
\begin{tabular}{c c} \hline \hline
**Exterior Wall Direction** & **Heat Transfer Coefficient (k)** \\ \hline Southeast (k1) & 0.66 \\ Northwest (k2) & 0.8 \\ Facing Exterior (k3) & 6.1 \\ Facing Exterior (k4) & 3.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Heat transfer coefficients for different exterior wall directions. Figure 6: Heat transfer coefficients of exterior walls in the window.