An Agent-Based Artificial Bee Colony (ABC) Algorithm for Hyperspectral Image Endmember Extraction in Parallel

Endmember extraction (EE) is an important process in hyperspectral image processing, and swarm intelligence algorithms have been developed to provide effective solutions for EE. However, these algorithms have limitations in terms of their calculation efficiency. To increase the computing speed of these algorithms by fully utilizing their potential parallel nature, this paper uses the artificial bee colony (ABC) algorithm to develop a multiagent system (MAS) for extracting endmembers from hyperspectral images. In this paper, EE is described as an optimization problem that involves the simplex volume and root-mean-square error (RMSE), and the ABC algorithm is used to obtain the optimal solution to the problem. To accelerate the execution of the ABC algorithm, it is incorporated into an established MAS platform that provides the advantages of high parallel computing efficiency, flexible system architecture, and responsive fault tolerance. Artificial bees and food sources, which are the two key components of the ABC algorithm, are implemented as standalone software agents. Different agents cooperate with each other via communication and produce the optimal solution. Comparative experiments are conducted to evaluate the performance of the agent-based ABC approach. The results indicate that the proposed agent-based ABC method can effectively solve the EE problem in distributive and high-speed computing environments.