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.
Ant Colony Optimization (ACO) is a classic swarm intelligence optimization algorithm that has been widely applied in various task scheduling scenarios. However, traditional ACO may easily get trapped in local optimal solutions. Inspired by Hybrid Breeding Optimization (HBO) algorithm and coevolution, this paper proposes a Heterogeneous Coevolution Ant Colony Optimization (HCEACO) algorithm based on hybrid breeding mechanisms to overcome the shortcomings of a single population in terms of solution diversity. Moreover, a strategy based on population similarity is proposed to determine whether communication is necessary after a fixed number of iterations, and to maintain a dynamic balance between population diversity and convergence speed in selecting communication partners. To fully validate the effectiveness of the proposed algorithm, multiple path planning algorithms are simulated and applied to multi-load Automatic Guided Vehicle (AGV) path planning. The experimental results show that the improved algorithm performs well in the multi-load AGV path planning problem, and has broad application prospects in this field.
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