# A Novel Method for Building Displacement Based on Multipopulation Genetic Algorithm
## 4 Experiments and Evaluations
### Analysis and Evaluation of Experimental Results  
In this experiment, we use the proposed method to resolve spatial conflicts by displacing buildings. The weighted distance of the triangulation-based skeleton is used to detect spatial conflicts, which considers the characteristics of buildings and is more cognitively appropriate than the minimum distance [24]. Figure 5a shows the displacement results of the proposed BDMPGA. Figure 5b shows a comparison of the original data and displacement results based on the BDMPGA, where the grey polygons are the original building data and the displacement results are represented by blue polygons. The basic statistical information of the displacement results based on the BDMPGA is listed in Table 2. From Figure 5 and Table 2, we find that the proposed BDMPGA can effectively resolve all possible spatial conflicts between buildings and buildings and between buildings and roads in each block and as much as possible to ensure the positional accuracy of buildings. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Crossover** & **Mutation** & **Population** & **Maintained Generation** \\  & **Probability** & **Probability** & **Number** & **Number** \\ \hline BDMPGA & [0.7, 0.9] & [0.001, 0.05] & 10 & 10 \\ GA & 0.8 & 0.008 & - & - \\ IGA & 0.8 & 0.008 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detailed parameter information of the three methods for building displacement. GA—genetic algorithm; IGA—immune genetic algorithm. To better evaluate the displacement results of the proposed method, we implemented two other intelligent optimization algorithms to perform comparative experiments. The first method is the GA, which was used by [PERSON] [17] to displace buildings. The other method is the IGA, which was proposed by [PERSON] [27] to improve the GA. The experimental results of the BDMPGA, GA, and IGA are compared using two evaluation indices: the number of unresolved conflicts and the total displacement distances of the buildings. The number of unresolved conflicts is the main evaluation index of the algorithm performance, indicating the ability of the algorithm to resolve spatial conflicts. The smaller this value is, the stronger the ability of the algorithm to resolve conflicts is. The total displacement distance indicates the disruption degree of the positional accuracy and spatial relationships. The smaller the value is, the better the positional accuracy and spatial relationship are. Figure 6a shows the displacement results of the GA. Figure 6b shows a comparison of the original data and displacement results based on the GA, where the grey polygons are the original building data, and the displacement results are represented by purple polygons. The basic statistical information of the displacement results based on the GA is listed in Table 3. From Table 3, it can be seen that the GA can resolve all the conflicts between buildings and buildings. However, it cannot resolve all the conflicts between the buildings and roads. There are three unresolved conflicts in block 2 and two unresolved conflicts in block 4. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Block**} & \multirow{3}{*}{ \begin{tabular}{c} **Building** \\ **Number** \\ \end{tabular}  } & \multicolumn{3}{c}{**Initial**} & \multirow{3}{*}{**MaxGen**} & \multirow{3}{*}{**Population**} & \multirow{3}{*}{**Number**} & \multirow{3}{*}{**Single**} & \multicolumn{3}{c}{**Final**} & \multirow{3}{*}{**Total**} \\ \cline{3-3} \cline{5-10}  & & & **<B-B>** & & & & & **Population** & & **<B-B>** & **<B-R>** \\ \cline{3-3} \cline{5-10}  & & **Conflict** & **Conflict** & & & & **Size** & & **Conflict** & **Conflict** & **Distances (m)** \\ \cline{3-3} \cline{5-10}  & 9 & 0 & 4 & 222 & 10 & 16 & 0 & 0 & 7.53 \\ \cline{3-3  
Figure 7a shows the displacement results of the IGA.