Description: The Gravitational Search Algorithm is a nature-inspired optimization method based on the law of gravity and mass interactions. This algorithm simulates the behavior of celestial bodies in space, where each potential solution to a problem is considered a mass that attracts other masses based on its quality or fitness. As the masses move through the solution space, the algorithm seeks to converge towards the best possible solution, similar to how planets orbit around stars. This approach allows for efficient exploration of large search spaces, making it particularly useful for complex optimization problems. The main features of the Gravitational Search Algorithm include its ability to avoid getting trapped in local optima, its adaptability to various types of optimization problems, and its efficiency in converging towards optimal solutions. Its relevance in the fields of machine learning and artificial intelligence lies in its application in hyperparameter optimization and other problems where the best configuration or solution needs to be found among a vast set of possibilities.
