# Harnessing Energy Balance and Genetic Algorithms for Efficient Building Demolition
## 3 Results  
In the experiments, we set p1 in Equation (3) to 0.1 and p2 in Equation (4) to 0.15. Based on extensive experimentation and engineering experience, we believe that within this error margin, the algorithm can converge rapidly. Furthermore, the pronounced changes during the iterative process are observable, facilitating the demonstration of the algorithm's stability and reliability. Our experimental variables fall into three categories:  
Shape and Size of Input Datasets: In real-world scenarios, different buildings often have distinct top-down shapes and sizes. We selected representative top-down views of buildings as our datasets. These include regular shapes such as U-shaped, T-shaped, and H-shaped shapes, as well as irregular shapes like polygons. Each shape is provided with varying areas, ensuring our experiments align closely with real-world applications and possess broad applicability. Number of Energy Release Points: In practical applications, buildings with larger areas typically have more energy release points, while those with smaller areas have fewer. In our experiments, we designated varying numbers of energy release points based on the area of the different datasets, mirroring real-world scenarios. Partitioning Strategy: We employed both uniform and random partitioning. The latter aligns more closely with real-world applications, where energy release points are randomly distributed across areas. Through algorithmic simulation, we aim to achieve a satisfactory final position, ensuring a balanced distribution of the area and energy diffusion. In our experiments, the number of partitioned regions matches the number of energy release points, meaning each region is assigned an initial energy release point. Table 1 presents the initial number of energy release points for each dataset, the number of pixels in each dataset (in units of 'k'), and their corresponding total areas (in square meters). After determining the shape, the size of the dataset, and the number of energy release points, we divided them into two groups based on a specific strategy. One group calculated the initial energy release points after evenly dividing the area, while the other group determined the energy source points after randomly dividing the area and calculated the energy balance boundaries and areas. These procedures have been detailed in previous sections. To ensure the accuracy of the results, we used the same dataset for both random and uniform divisions. Upon dividing the area and determining the initial energy release points, we commenced the execution of the algorithm. The steps were identical for both uniformly and randomly divided datasets. After achieving multi-source energy balance, some data already met our error range. However, for some areas, despite reaching an energy balance, there was a significant discrepancy in the sizes of different regions. In such cases, we initiated our improved genetic algorithm for optimization. The process concluded upon meeting the set conditions, outputting the results. We will showcase our experimental results in the following sections. In Section 3.1, we will showcase the iterative processes for the U-shaped, H-shaped, T-shaped, and polygonal datasets using both uniform and random segmentation strategies. In Section 3.2, we conducted ten experiments, tallying the number of equilibrium iterations and the number of iterations in the genetic algorithm for each experiment. Additionally, we used a line chart to display the ratio of the maximum to minimum area differences in the ten experiments. It is worth noting that our experimental procedure involves multiple iterations, with each iteration producing a corresponding result diagram. We have selected and displayed diagrams from the iterations that depict significant changes and represent the entire process. In the images we presented, the small circles represent the positions of the energy release points determined each time.