Extended Trail Reinforcement Strategies for Ant Colony Optimization

  • Nikola Ivkovic
  • Mirko Malekovic
  • Marin Golub
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7076)


Ant colony optimization (ACO) is a metaheuristic inspired by the foraging behavior of biological ants that was successfully applied for solving computationally hard problems. The fundamental idea that drives the ACO is the usage of pheromone trails for accumulating experience about the problem that is been solved. The best performing ACO algorithms typically use one, in some sense “the best”, solution to reinforce trail components. Two main trail reinforcement strategies are used in ACO algorithms: iteration best and global best strategy. This paper extends the reinforcement strategies by using the information from an arbitrary number of previous iterations of the algorithm. The influence of proposed strategies on algorithmic behavior is analyzed on different classes of optimization problems. The conducted experiments showed that using the proposed strategies can improve the algorithm’s performance. To compare the strategies we use the Mann–Whitney and Kruskal – Wallis statistical tests.


reinforcement strategy pheromone trail MAX-MIN ant system Ant colony optimization Swarm intelligence combinatorial optimization parameter settings 


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  1. 1.
    Dorigo, M., Di Caro, G.: Ant colony optimization: A new meta-heuristic. In: Angeline, P.J., et al. (eds.) Proceedings of the Congress on Evolutionary Computation, vol. 2, pp. 1470–1477. IEEE Press (1999)Google Scholar
  2. 2.
    Dorigo, M., Di Caro, G., Gambardella, L.M.: Ant Algorithms for Discrete Optimization. Artificial Life 5(2), 137–172 (1999)CrossRefGoogle Scholar
  3. 3.
    Dorigo, M., Maniezzo, V., Colorni, A.: The Ant System: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics – Part B 26, 29–42 (1996)CrossRefGoogle Scholar
  4. 4.
    Dorigo, M., Gambardella, L.M.: Ant Colony System: A Cooperative Learning Approach to the Travelling Salesman Problem. IEEE Transactions on Evolutionary Computation 1(1), 53–66 (1997)CrossRefGoogle Scholar
  5. 5.
    Dorigo, M.: Ant colony optimization. Scholarpedia 2(3), 1461, revision #82084 (2007)Google Scholar
  6. 6.
    Stützle, T., Hoos, H.H.: MAX–MIN Ant System. Future Generation Computer Systems 16(9), 889–914 (2000)CrossRefzbMATHGoogle Scholar
  7. 7.
    Abraham, A., Konar, A., Samal, N.R., Das, S.: Stability analysis of the ant system dynamics with non-uniform pheromone deposition rules. IEEE Congress on Evolutionary Computation, 1103–1108 (2007)Google Scholar
  8. 8.
    Derrac, J., García, S., Molina, D., Herrera, F.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation 1(1), 3–18 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Nikola Ivkovic
    • 1
  • Mirko Malekovic
    • 1
  • Marin Golub
    • 2
  1. 1.Faculty of Organization and InformaticsUniversity of ZagrebCroatia
  2. 2.Faculty of Electrical Engineering and ComputingUniversity of ZagrebCroatia

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