Advertisement

Novel Fish Swarm Heuristics for Bound Constrained Global Optimization Problems

  • Ana Maria A. C. Rocha
  • Edite M. G. P. Fernandes
  • Tiago F. M. C. Martins
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6784)

Abstract

The heuristics herein presented are modified versions of the artificial fish swarm algorithm for global optimization. The new ideas aim to improve solution accuracy and reduce computational costs, in particular the number of function evaluations. The modifications also focus on special point movements, such as the random, search and the leap movements. A local search is applied to refine promising regions. An extension to bound constrained problems is also presented. To assess the performance of the two proposed heuristics, we use the performance profiles as proposed by Dolan and Moré in 2002. A comparison with three stochastic methods from the literature is included.

Keywords

Global optimization Derivative-free method Swarm intelligence Heuristics 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ali, M.M., Khompatraporn, C., Zabinsky, Z.B.: A numerical evaluation of several stochastic algorithms on selected continuous global optimization test problems. Journal of Global Optimization 31, 635–672 (2005)CrossRefzbMATHMathSciNetGoogle Scholar
  2. 2.
    Birattari, M., Dorigo, M.: How to assess and report the performance of a stochastic algorithm on a benchmark problem: mean or best result on a number of runs? Optimization Letters 1, 309–311 (2007)CrossRefzbMATHMathSciNetGoogle Scholar
  3. 3.
    Dolan, E.D., Moré, J.J.: Benchmarking optimization software with performance profiles. Mathematical Programming 91, 201–213 (2002)CrossRefzbMATHMathSciNetGoogle Scholar
  4. 4.
    Fernandes, E.M.G.P., Martins, T.F.M.C., Rocha, A.M.A.C.: Fish swarm intelligent algorithm for bound constrained global optimization. In: Aguiar, J.V. (ed.) CMMSE 2009, pp. 461–472 (2009) ISBN: 978-84-612-9727-6Google Scholar
  5. 5.
    Hansen, N.: The CMA evolution strategy: a comparing review, In: Lozano, J.A., Larranaga, P., Inza, I., Bengoetxea, E. (eds.), Towards a New Evolutionary Computation. Advances on Estimation of Distribution Algorithms, pp. 75–102 (2006)Google Scholar
  6. 6.
    Ingber, L.: Adaptive simulated annealing (ASA): lessons learned. Control and Cybernetics 25, 33–54 (1996)zbMATHGoogle Scholar
  7. 7.
    Jiang, M., Mastorakis, N., Yuan, D., Lagunas, M.A.: Image segmentation with improved artificial fish swarm algorithm. In: Mastorakis, N., Mladenov, V., Kontargyri, V.T. (eds.) ECC 2008. Lecture Notes in Electrical Engineering, vol. 28, pp. 133–138. Springer, Heidelberg (2009) ISBN: 978-0-387-84818-1 Google Scholar
  8. 8.
    Jiang, M., Wang, Y., Pfletschinger, S., Lagunas, M.A., Yuan, D.: Optimal multiuser detection with artificial fish swarm algorithm. In: Huang, D.-S., Heutte, L., Loog, M. (eds.) ICIC 2007. CCIS, vol. 2, pp. 1084–1093. Springer, Heidelberg (2007)Google Scholar
  9. 9.
    Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization 39, 459–471 (2007)CrossRefzbMATHMathSciNetGoogle Scholar
  10. 10.
    Karimi, A., Nobahari, H., Siarry, P.: Continuous ant colony system and tabu search algorithms hybridized for global minimization of continuous multi-minima functions. Computational Optimization and Applications 45, 639–661 (2010)CrossRefzbMATHMathSciNetGoogle Scholar
  11. 11.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: IEEE International Conference on Neural Network, pp. 1942–1948 (1995)Google Scholar
  12. 12.
    Mittelmann, H.D., Pruessner, A.: A server for automated performance analysis of benchmarking data. Optimization Methods and Software 21, 105–120 (2006)CrossRefzbMATHGoogle Scholar
  13. 13.
    Montgomery, D.C.: Design and Analysis of Experiments, 5th edn. John Wiley & Sons, Chichester (2002)Google Scholar
  14. 14.
    Rocha, A.M.A.C., Fernandes, E.M.G.P.: Hybridizing the electromagnetism-like algorithm with descent search for solving engineering design problems. International Journal of Computer Mathematics 86, 1932–1946 (2009)CrossRefzbMATHGoogle Scholar
  15. 15.
    Socha, K., Dorigo, M.: Ant colony optimization for continuous domains. European Journal of Operational Research 185, 1155–1173 (2008)CrossRefzbMATHMathSciNetGoogle Scholar
  16. 16.
    Stanoyevitch, A.: Homogeneous genetic algorithms. International Journal of Computer Mathematics 87, 476–490 (2010)CrossRefzbMATHMathSciNetGoogle Scholar
  17. 17.
    Vaz, A.I.F., Vicente, L.N.: A particle swarm pattern search method for bound constrained global optimization. Journal of Global Optimization 39, 197–219 (2007)CrossRefzbMATHMathSciNetGoogle Scholar
  18. 18.
    Wang, C.-R., Zhou, C.-L., Ma, J.-W.: An improved artificial fish-swarm algorithm and its application in feed-forward neural networks. In: Proceedings of the 4th ICMLC, pp. 2890–2894 (2005)Google Scholar
  19. 19.
    Wang, X., Gao, N., Cai, S., Huang, M.: An artificial fish swarm algorithm based and ABC supported qoS unicast routing scheme in NGI. In: Min, G., Di Martino, B., Yang, L.T., Guo, M., Rünger, G. (eds.) ISPA Workshops 2006. LNCS, vol. 4331, pp. 205–214. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  20. 20.
    Zahara, E., Hu, C.-H.: Solving constrained optimization problems with hybrid particle swarm optimization. Engineering Optimization 40(11), 1031–1049 (2008)CrossRefMathSciNetGoogle Scholar
  21. 21.
    Zhang, C., Ning, J., Ouyang, D.: A hybrid alternate two phases particle swarm optimization algorithm for flow shop scheduling problem. Computers & Industrial Engineering 58, 1–11 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ana Maria A. C. Rocha
    • 1
  • Edite M. G. P. Fernandes
    • 2
  • Tiago F. M. C. Martins
    • 2
  1. 1.Department of Production and SystemsUniversity of MinhoBragaPortugal
  2. 2.Algoritmi R&D CentreUniversity of MinhoBragaPortugal

Personalised recommendations