AN ADAPTIVE MULTI-AGENT ACO CLUSTERING ALGORITHM
This work is granted by Railway Ministry Science and Technology Research Project. (Grant No. 2003X037-A).
An adaptive multi-agent ACO clustering algorithm is proposed in this paper to enhance the efficiency and quality of ant based clustering. The algorithm includes three levels: Level-0 agents build solutions, level-1 agents improve solutions and level-2 agents update pheromone matrix. The updated pheromone then provides feedback information for the next iteration of solution construction. Mutation probability and pheromone resistance are the adaptive parameters, which can be adjusted automatically during the evolution progress to solve the contradiction between convergence speed and precocity and stagnation. Experimental results show that the proposed algorithm is more effective compared with the clustering algorithm based on GA and k-means, and the clustering quality and efficiency are promising.
