The increasing contamination of water bodies by low-density pollutants, such as oil and its derivatives, represents a significant environmental challenge, affecting aquatic ecosystems and coastal economies. Conventional response relies on floating booms that teams must position and maintain, present limitations due to adverse environmental factors like winds and ocean currents. This study proposes an innovative approach based on swarm intelligence that automates the assembly, positioning, and dynamic stabilization of the same type of physical boom, each autonomous surface vehicle is equipped with a short boom segment. Inspired by collective behaviors in nature, the method combines the Flocking algorithm and Particle Swarm Optimization, enabling mobile agents to detect and surround the contaminated area in a distributed and efficient manner. Simulations were conducted to evaluate the effectiveness of the technique in different scenarios, considering pollutant patches with both geometric and abstract shapes. The results demonstrated that the method can form stable barriers, adapting to variations in the shape and initial positions of the agents. Additionally, the approach proved robust against environmental challenges, ensuring effective pollutant containment without the need for direct human intervention.