Efficient band selection via multi-agent system and manta ray foraging optimization

Efficient feature selection is essential for processing hyperspectral images due to their high dimensionality and computational complexity. This paper proposes a band selection method based on a multi-agent system, aiming to reduce dimensionality, decrease computational expenses, and enhance data processing efficiency. The method decomposes the optimization problem into exploration and exploitation tasks, promoting collaboration among multiple agents to search for global optimal solutions in the feature subset space. Additionally, adaptive search boundary design is employed to adjust the search strategy, enhancing search efficiency. Experimental results demonstrate the superiority of the proposed method in band selection tasks, showing significant advantages in search efficiency and stability compared to traditional methods. These findings highlight the potential of the proposed method for practical applications.