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A Hybrid Algorithm Based on Ant Colony System and Learning Automata for Solving Steiner Tree Problem

S. Noferesti, M. Rajaei


In this paper we propose an iterative algorithm based on ant colony system and learning automata for solving Steiner tree problem. Ant colony algorithms have many parameters. The appropriate selections of these parameters have large effects on the performance and convergence of the algorithm. In this paper, we use learning automata for parameter adaptation of ant colony algorithms. The experiments based on the benchmarks from category B of Steiner tree problem in the OR-library have been carried out to demonstrate the effectiveness of the proposed algorithm. Compared with traditional heuristic algorithms, the proposed algorithm obtains more promising results. And it also performs better than the ant colony system without parameter adaptation.


Steiner tree, learning automata, ant colony system, hard problems, parameter adaption

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