Particle Swarm Optimization (PSO) is a computational method inspired by the social behavior of birds and fish. In this technique, a group of potential solutions, called "particles," moves through a problem space, adjusting their positions based on their own experiences and those of their neighbors. Each particle represents a possible solution, and they work together to find the best one by sharing information about their successes. As the particles explore the space, they update their velocities and positions, gradually converging towards the optimal solution. PSO is widely used in various fields, including machine learning, engineering, and economics, due to its simplicity and effectiveness in solving complex optimization problems.