Why the Intelligent Water Drops Cannot Be Considered as a Novel Algorithm

  • Christian Leonardo Camacho-VillalónEmail author
  • Marco Dorigo
  • Thomas Stützle
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11172)


In this paper we show that intelligent water drops (IWD), a swarm intelligence based approach to discrete optimization proposed by Shah-Hosseini in 2007, is a particular instantiation of the ant colony optimization (ACO) metaheuristic. To do so, in the paper, we identify the components of IWD and place them into the ACO metaheuristic framework. We show therefore that there was no need for a new natural metaphor. We also discuss that the proposed metaphor does not bring any novel insight into the algorithmic optimization process used by IWD.


Intelligent water drops Ant colony optimization Novel algorithm 



Marco Dorigo and Thomas Stützle acknowledge support from the Belgian F.R.S.-FNRS, of which they are Research Directors.


  1. 1.
    Alaya, I., Solnon, C., Ghédira, K.: Ant colony optimization for multi-objective optimization problems. In: 19th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2007), vol. 1, pp. 450–457. IEEE Computer Society Press, Los Alamitos, CA (2007)Google Scholar
  2. 2.
    Askarzadeh, A.: Bird mating optimizer: an optimization algorithm inspired by bird mating strategies. Commun. Nonlinear Sci. Numer. Simul. 19(4), 1213–1228 (2014)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Birattari, M., Balaprakash, P., Dorigo, M.: The ACO/F-RACE algorithm for combinatorial optimization under uncertainty. In: Doerner, K.F., Gendreau, M., Greistorfer, P., Gutjahr, W.J., Hartl, R.F., Reimann, M. (eds.) Metaheuristics - Progress in Complex Systems Optimization, Operations Research/Computer Science Interfaces Series, vol. 39, pp. 189–203. Springer, New York (2006). Scholar
  4. 4.
    Blum, C.: Beam-ACO–hybridizing ant colony optimization with beam search: an application to open shop scheduling. Comput. Oper. Res. 32(6), 1565–1591 (2005)CrossRefGoogle Scholar
  5. 5.
    Blum, C., Dorigo, M.: The hyper-cube framework for ant colony optimization. IEEE Trans. Syst. Man, Cybern. - Part B 34(2), 1161–1172 (2004)CrossRefGoogle Scholar
  6. 6.
    Bullnheimer, B., Hartl, R.F., Strauss, C.: An improved ant system algorithm for the vehicle routing problem. Ann. Oper. Res. 89, 319–328 (1999)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Cordón, O., de Viana, I.F., Herrera, F., Moreno, L.: A new ACO model integrating evolutionary computation concepts: the best-worst ant system. In: Dorigo, M., et al. (eds.) Abstract Proceedings of ANTS 2000 - From Ant Colonies to Artificial Ants: Second International Workshop on Ant Algorithms, pp. 22–29. IRIDIA, Université Libre de Bruxelles, Belgium, 7–9 September 2000Google Scholar
  8. 8.
    Cuevas, E., Miguel, C., Zaldívar, D., Pérez-Cisneros, M.: A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Syst. Appl. 40(16), 6374–6384 (2013)CrossRefGoogle Scholar
  9. 9.
    Deneubourg, J.L., Aron, S., Goss, S., Pasteels, J.M.: The self-organizing exploratory pattern of the Argentine Ant. J. Insect Behav. 3(2), 159–168 (1990). Scholar
  10. 10.
    Dorigo, M.: Optimization, Learning and Natural Algorithms. Ph.D. thesis, Dipartimento di Elettronica, Politecnico di Milano, Italy (1992). (in Italian)Google Scholar
  11. 11.
    Dorigo, M., Gambardella, L.M.: A study of some properties of Ant-Q. In: Voigt, H.-M., Ebeling, W., Rechenberg, I., Schwefel, H.-P. (eds.) PPSN 1996. LNCS, vol. 1141, pp. 656–665. Springer, Heidelberg (1996). Scholar
  12. 12.
    Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)CrossRefGoogle Scholar
  13. 13.
    Dorigo, M., Maniezzo, V., Colorni, A.: The Ant System: An autocatalytic optimizing process. Technical report, 91–016 Revised, Dipartimento di Elettronica, Politecnico di Milano, Italy (1991)Google Scholar
  14. 14.
    Dorigo, M., Maniezzo, V., Colorni, A.: Positive feedback as a search strategy. Technical report, 91–016, Dipartimento di Elettronica, Politecnico di Milano, Italy (1991)Google Scholar
  15. 15.
    Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. - Part B 26(1), 29–41 (1996)CrossRefGoogle Scholar
  16. 16.
    Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge, MA (2004)zbMATHGoogle Scholar
  17. 17.
    Gambardella, L.M., Dorigo, M.: Ant-Q: a reinforcement learning approach to the traveling salesman problem. In: Proceedings of the Twelfth International Conference on Machine Learning, ML 1995, pp. 252–260. Morgan Kaufmann Publishers, Palo Alto (1995)CrossRefGoogle Scholar
  18. 18.
    Guntsch, M., Middendorf, M.: A population based approach for ACO. In: Cagnoni, S., Gottlieb, J., Hart, E., Middendorf, M., Raidl, G.R. (eds.) EvoWorkshops 2002. LNCS, vol. 2279, pp. 72–81. Springer, Heidelberg (2002). Scholar
  19. 19.
    Machado, L., Schirru, R.: The Ant-Q algorithm applied to the nuclear reload problem. Ann. Nucl. Energy 29(12), 1455–1470 (2002)CrossRefGoogle Scholar
  20. 20.
    Maniezzo, V.: Exact and approximate nondeterministic tree-search procedures for the quadratic assignment problem. INFORMS J. Comput. 11(4), 358–369 (1999)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Melvin, G., Dodd, T.J., Groß, R.: Why ‘GSA: a gravitational search algorithm’ is not genuinely based on the law of gravity. Natural Comput. 11(4), 719–720 (2012)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)CrossRefGoogle Scholar
  23. 23.
    Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)CrossRefGoogle Scholar
  24. 24.
    Piotrowski, A.P., Napiorkowski, J.J., Rowinski, P.M.: How novel is the “novel” black hole optimization approach? Inf. Sci. 267, 191–200 (2014)CrossRefGoogle Scholar
  25. 25.
    Shah-Hosseini, H.: Problem solving by intelligent water drops. In: Proceedings of the 2007 Congress on Evolutionary Computation, CEC 2007, pp. 3226–3231. IEEE Press, Piscataway (2007)Google Scholar
  26. 26.
    Shah-Hosseini, H.: Intelligent water drops algorithm: a new optimization method for solving the multiple knapsack problem. Int. J. Intell. Comput. Cybern. 1(2), 193–212 (2008)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Shah-Hosseini, H.: The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm. Int. J. Bio-Inspired Comput. 1(1–2), 71–79 (2009)CrossRefGoogle Scholar
  28. 28.
    Socha, K., Dorigo, M.: Ant colony optimization for continuous domains. Eur. J. Oper. Res. 185(3), 1155–1173 (2008). Scholar
  29. 29.
    Sörensen, K.: Metaheuristics–the metaphor exposed. Int. Trans. Oper. Res. 22(1), 3–18 (2015). Scholar
  30. 30.
    Stützle, T., Hoos, H.H.: The \({\cal{MAX-MIN}}\) and local search for the traveling salesman problem. In: Bäck, T., Michalewicz, Z., Yao, X. (eds.) Proceedings of the 1997 IEEE International Conference on Evolutionary Computation, ICEC 1997, pp. 309–314. IEEE Press, Piscataway (1997)Google Scholar
  31. 31.
    Stützle, T., Hoos, H.H.: \(\cal{MAX--MIN}\) Ant system. Future Gener. Comput. Syst. 16(8), 889–914 (2000)CrossRefGoogle Scholar
  32. 32.
    Weyland, D.: A rigorous analysis of the harmony search algorithm: how the research community can be misled by a “novel” methodology. Int. J. Appl. Metaheuristic Comput. 12(2), 50–60 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.IRIDIA, Université Libre de BruxellesBrusselsBelgium

Personalised recommendations