Advertisement

Ant Colony Optimization Start Strategies Performance According Some of the Parameters

  • Stefka Fidanova
  • Pencho Marinov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8236)

Abstract

Ant Colony Optimization (ACO) is a stochastic search method that mimic the social behavior of real ants colonies, which manage to establish the shortest rout to feeding sources and back. Such algorithms have been developed to arrive at near-optimal solutions to large-scale optimization problems, for which traditional mathematical techniques may fail. On this paper is proposed an ant algorithm with semi-random start. Several start strategies are prepared at the basis of the start nodes estimation. There are several parameters which manage the starting strategies. In this work we focus on influence on the quality of the achieved solutions of the parameters which shows the percentage of the solutions classified as good and as bad respectively. This new technique is tested on Multiple Knapsack Problem (MKP).

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Dorigo, M., Gambardella, L.M.: Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem. IEEE Transactions on Evolutionary Computation 1, 53–66 (1997)CrossRefGoogle Scholar
  2. 2.
    Dorigo, M., Stutzle, T.: Ant Colony Optimization. MIT Press (2004)Google Scholar
  3. 3.
    Fidanova, S.: Evolutionary Algorithm for Multiple Knapsack Problem. In: Int. Conference Parallel Problems Solving from Nature, Real World Optimization Using Evolutionary Computing, Granada, Spain (2002) ISBN No 0-9543481-0-9Google Scholar
  4. 4.
    Fidanova, S.: Ant colony optimization and multiple knapsack problem. In: Renard, J.P. (ed.) Handbook of Research on Nature Inspired Computing for Economics ad Management, pp. 498–509. Idea Grup Inc. (2006) ISBN 1-59140-984-5Google Scholar
  5. 5.
    Fidanova, S., Atanassov, K., Marinov, P., Parvathi, R.: Ant Colony Optimization for Multiple Knapsack Problems with Controlled Starts. Int. J. Bioautomation 13(4), 271–280Google Scholar
  6. 6.
    Fidanova, S., Marinov, P.: Intuitionistic Fuzzy Estimation of the Ant Methodology. J. of Cybernetics and Information Technologies 9(2), 79–88 (2009) ISSN 1311-9702Google Scholar
  7. 7.
    Fiodanova, S., Marinov, P., Atanassov, K.: Generalized Net Models of the Process of Anmt Colony Optimization with Different Strategies and Intuitionistic Fuzzy Estimations. In: Proc. Jangjeon Math., vol. 13(1), pp. 1–12 (2010) ISSN 1598-7264, Soc.Google Scholar
  8. 8.
    Fidanova, S., Atanassov, K., Marinov, P.: Start Strategies of ACO Applied on Subset Problems. In: Dimov, I., Dimova, S., Kolkovska, N. (eds.) NMA 2010. LNCS, vol. 6046, pp. 248–255. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Fidanova, S., Marinov, P., Atanassov, K.: Sensitivity Analysis of ACO Start Strategies for Subset Problems. In: Dimov, I., Dimova, S., Kolkovska, N. (eds.) NMA 2010. LNCS, vol. 6046, pp. 256–263. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  10. 10.
    Fidanova, S., Atanassov, K., Marinov, P.: Intuitionistic Fuzzy Estimation of the Ant Colony Optimization Starting Points. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds.) LSSC 2011. LNCS, vol. 7116, pp. 222–229. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  11. 11.
    Reiman, M., Laumanns, M.: A Hybrid ACO algorithm for the Capacitated Minimum Spanning Tree Problem. In: Proc. of First Int. Workshop on Hybrid Metahuristics, Valencia, Spain, pp. 1–10 (2004)Google Scholar
  12. 12.
    Stutzle, T., Dorigo, M.: ACO Algorithm for the Traveling Salesman Problem. In: Miettinen, K., Makela, M., Neittaanmaki, P., Periaux, J. (eds.) Evolutionary Algorithms in Engineering and Computer Science, pp. 163–183. Wiley (1999)Google Scholar
  13. 13.
    Zhang, T., Wang, S., Tian, W., Zhang, Y.: ACO-VRPTWRV: A New Algorithm for the Vehicle Routing Problems with Time Windows and Re-used Vehicles based on Ant Colony Optimization. In: Sixth International Conference on Intelligent Systems Design and Applications, pp. 390–395. IEEE Press (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Stefka Fidanova
    • 1
  • Pencho Marinov
    • 1
  1. 1.Institute of Information and Communication TechnologiesBulgarian Academy of SciencesSofiaBulgaria

Personalised recommendations