# Sustainable Parking Space Management Using Machine Learning and Swarm Theory--The SPARK System
## 3 Methodology Concept
### Applied Algorithms and Technologies in This Study  
Among the various methods used to solve optimization problems, the ABC algorithm is employed in this study to address the problem at hand. First introduced by [PERSON] [74], the ABC algorithm stands out as a bio-inspired optimization method modeled on the foraging behavior of honey bees. This algorithm belongs to a broader class of nature-inspired techniques, such as ant colony optimization and particle swarm optimization, which are widely applied to complex optimization challenges. In general, the ABC algorithm operates through a series of iterative steps, as illustrated in Figure 1, based on the theoretical flow detailed by [PERSON] [75], continuously improve potential solutions. This visual representation clarifies its operational steps, as each bio-inspired method utilizes a different strategy. The population of the ABC algorithm is structured as a colony of bees, which includes three main types: employed bees, onlooker bees, and scout bees. Each employed bee is responsible for a specific food source, continuously searching for better solutions in the  
Figure 1: Theoretical workflow of the ABC algorithm (Source: Author’s own elaboration based on [PERSON] [75]). nearby region, while onlooker bees evaluate and choose the best food sources based on their quality or fitness. If an employed bee can no longer improve its food source, it transitions into a scout bee, randomly searching for a new food source. The algorithm begins by randomly initializing the population, where each food source represents a potential solution to the optimization problem. This initial step allows for a broad exploration of the problem space, increasing the likelihood of finding optimal solutions. Employed bees then begin searching for improved solutions by generating new food sources in the vicinity of current ones, and if a new solution proves superior, it replaces the existing one. This process ensures continuous refinement of the solutions through local searches. Onlooker bees play a key role in the decision-making process. They select food sources for further exploration based on a probabilistic model, favoring higher-quality solutions. This approach helps improve the efficiency of the search by directing attention to the most promising solutions. At times, if no improvement can be made after several attempts, the associated bee becomes a scout, abandoning the current food source and searching randomly for a new one. This mechanism is essential for maintaining the algorithm's global search capabilities, preventing it from getting stuck in local optima. The algorithm terminates when a predefined number of iterations is reached, or when an acceptable solution has been found. These parameters can be customized depending on the complexity of the problem at hand [74; 75; 76]. In summary, the ABC algorithm represents a metaheuristic approach inspired by the behavior of bee colonies, where potential solutions to an optimization problem are modeled as food sources. The bees, acting as employed, onlooker, and scout bees, coordinate the processes of local exploitation and global exploration of the solution space. The primary goal of the algorithm is to achieve optimization through the continuous improvement of existing solutions and the introduction of new ones, where the \"quality of the food\" corresponds to the objective function value, and the exploration mechanisms prevent convergence to local minima. For further details, refer to the foundational studies by [PERSON] [74; 76]. In this work, image segmentation plays a crucial role, enabling precise analysis of parking scenes by identifying and separating individual objects within the image. Segmentation is the process of dividing an image into semantically meaningful regions, allowing for the accurate determination of the shape and position of objects such as vehicles or available parking spaces.