# Harnessing Energy Balance and Genetic Algorithms for Efficient Building Demolition
## 3 Results
### Stability Verification  
In this section, we conducted ten experiments to compare the area discrepancies under both the average allocation strategy and the random allocation strategy. Figure 19 illustrates the area discrepancy under the random allocation strategy, while Figure 20 depicts that under the average allocation strategy.  
With the random allocation strategy, the discrepancy in the area is significantly associated with the initial position of the energy release point and the crossover mutation in the genetic algorithm, resulting in a larger fluctuation in its outcomes. In contrast, under the average allocation strategy, since the initial position of the energy release point remains constant, only minor differences emerge when random crossover and mutation are applied to the lower six digits of the central coordinates in the genetic algorithm. This explains the more stable discrepancy observed with the average allocation strategy.  
Figure 19: Average Distribution Strategy Area Difference Statistics.  
Figure 20: Random Assignment Strategy Area Difference Statistics.  
Throughout the tests, the area discrepancy consistently remained within the range of 14-15%, aligning with our preset error threshold of 15%. These experiments robustly demonstrate the stability of our method.  
After quantifying the area discrepancies, the number of iterations also became a focal point of the study. We established specific area discrepancy objectives to reduce the number of iterations and rapidly achieve an area equilibrium.  
In the average allocation strategy, for datasets with regular shapes, such as the U-shaped, T-shaped, and H-shaped datasets, we assigned the same initial positions in each dataset, all located at the center of the averagely divided area. Therefore, they can quickly achieve an energy equilibrium. However, in the genetic algorithm segment, since our ultimate goal is to have area discrepancies within the predetermined threshold, this is closely related to the shape of the dataset. For U-shaped, H-shaped, and T-shaped data, although they can quickly reach an energy balance, the area discrepancy is often larger, necessitating multiple iterations of the genetic algorithm. Generally, the more regular the shape, the quicker the genetic algorithm can achieve area balance. Due to the fixed starting point position in the average distribution strategy, the number of times energy equilibrium is achieved remains constant across multiple experiments. Therefore, under this strategy, we present the iteration count of the genetic algorithm. Figure 21 illustrates the number of times the genetic algorithm is executed under this strategy  
In the above figure, we can observe that regardless of the scenario, the difference in our iteration counts does not exceed three times. This demonstrates that our algorithm requires fewer iterations, and the area discrepancy also lies between 14 and 15%, which aligns with our expectations.  
Our experiment elaborately discusses the energy balance iteration process and the improved genetic algorithm iteration process under different strategies for H-shaped, U-shaped, T-shaped, and polygonal configurations. The experiment demonstrates that our method yields smaller error margins and requires fewer iterations, effectively addressing the issue of energy and area imbalance in multi-source point energy release scenarios. The experimental results are compelling and present a strong case for the efficacy of our proposed method.  
Figure 23: Number of genetic algorithm iterations in the random allocation strategy.  
Figure 22: Number of energy balance iterations in the random allocation strategy.