# Harnessing Energy Balance and Genetic Algorithms for Efficient Building Demolition
## 1 Introduction
Although the aforementioned research involves building demolition and energy release, it only considers the internal structure of the building, neglecting the energy release on the surface, and it does not take into account the diversity of building structures. Moreover, it does not consider the process and results of multi-point energy release. In optimization strategies, genetic algorithms and simulated annealing algorithms are more often used to study house energy distributions and house structure layouts and have not been combined with building demolition and multi-point energy release on the building surface. In order to achieve a balance at the boundary during energy release from multiple energy release points and to make the area of each energy release coverage area as uniform as possible, this study presents an optimization strategy that combines genetic algorithms with a potential function to achieve an energy equilibrium. Initially, each release point is viewed as a source of energy diffusion. The energy spread is then modeled using a potential function until the boundary energy of each diffusion area stabilizes. Subsequently, the center of the diffusion range is used as a new reference point for further energy diffusion calculations. This iterative process continues until the difference between two consecutive calculations is within a predefined error margin, ensuring a balanced energy distribution. If the difference between the largest and smallest diffusion areas surpasses a set threshold, a secondary optimization using the genetic algorithm is triggered. During this phase, the effective energy coverage area for each release is identified, and its center coordinates (i.e., release points) are encoded into a 12-bit binary format. By selecting the encodings corresponding to the largest and smallest energy coverage areas, two random bits from the lower six bits are subjected to crossover and mutation operations. The resulting new encoding then becomes the new release point for the next round of energy balance calculations. This optimization cycle repeats until the difference between the largest and smallest energy coverage areas meets the acceptable error range. The primary contributions of this study can be summarized in the following areas: First, we propose a unique optimization strategy that addresses the challenges of overlapping coverage and uneven energy distribution at diffusion points, leading to improved precision and efficiency in energy dissemination. Second, by merging an enhanced genetic algorithm with potential function energy equalization, our research presents a practical method for refining energy dissemination strategies. Finally, through empirical testing, we demonstrate that our proposed optimization approach performs well in terms of the precision and effectiveness of energy dissemination across diverse architectural structures and initial diffusion point placements, providing a valuable perspective in the technical domain. The organization of this paper is as follows: Section 2 thoroughly introduces our dataset, research methodology, and the algorithms employed, accompanied by mathematical models and an analysis of algorithmic complexity. Section 3 exhibits the experimental outcomes, encompassing iterative graphs and the computational results of numerous ex periments. Lastly, Section 4 encapsulates the research and underscores potential avenues for future exploration.