Advertisement

Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems

  • Dervis Karaboga
  • Bahriye Basturk
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4529)

Abstract

This paper presents the comparison results on the performance of the Artificial Bee Colony (ABC) algorithm for constrained optimization problems. The ABC algorithm has been firstly proposed for unconstrained optimization problems and showed that it has superior performance on these kind of problems. In this paper, the ABC algorithm has been extended for solving constrained optimization problems and applied to a set of constrained problems .

Keywords

Particle Swarm Optimization Constrain Optimization Problem Objective Function Evaluation Nectar Amount Optimal Particle Swarm Optimization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Parsopoulos, K.E., Vrahatis, M.N.: Particle Swarm Optimization Method for Constrained Optimization Problems. In: Intelligent Technologies - Theory and Applications: New Trends in Intelligent Technologies, pp. 214–220. IOS Press, Amsterdam (2002)Google Scholar
  2. 2.
    Hedar, A.R., Fukushima, M.: Derivative-Free Filter Simulated Annealing Method for Constrained Continuous Global OptimizationGoogle Scholar
  3. 3.
    Michalewicz, Z., Schoenauer, M.: Evolutionary Algorithms for Constrained Parameter Optimization Problems. Evolutionary Computation 4(1), 1–32 (1995)CrossRefGoogle Scholar
  4. 4.
    Floudas, C.A., Pardalos, P.M. (eds.): A Collection of Test Problems for Constrained Global Optimization Algorithms. LNCS, vol. 455. Springer, Heidelberg (1990)zbMATHGoogle Scholar
  5. 5.
    Himmelblau, D.M.: Applied Nonlinear Programming. McGraw-Hill, New York (1972)zbMATHGoogle Scholar
  6. 6.
    Joines, J.A., Houck, C.R.: On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with gas. In: Proc. IEEE Int. Conf. Evol. Comp., pp. 579–585 (1994)Google Scholar
  7. 7.
    Hu, X., Eberhart, R.C.: Solving constrained nonlinear optimization problems with particle swarm optimization. In: Proceedings of the Sixth World Multiconference on Systemics, Cybernetics and Informatics (2002)Google Scholar
  8. 8.
    Hu, X., Eberhart, R.C., Shi, Y.H.: Engineering optimization with particle swarm. In: IEEE Swarm Intelligence Symposium, pp. 53–57 (2003)Google Scholar
  9. 9.
    Parsopoulos, K.E., Vrahatis, M.N.: Unified Particle Swarm Optimization for Solving Constrained Engineering Optimization Problems. In: Wang, L., Chen, K., Ong, Y.S. (eds.) ICNC 2005. LNCS, vol. 3612, pp. 582–591. Springer, Heidelberg (2005)Google Scholar
  10. 10.
    Zavala, A.E.M., Aguirre, A.H., Diharce, E.R.V.: Constrained optimization via particle evolutionary swarm optimization algorithm (PESO). In: Proceedings of the 2005 conference on Genetic and evolutionary computation (GECCO’05), pp. 209–216 (2005)Google Scholar
  11. 11.
    Karaboga, D.: An Idea Based On Honey Bee Swarm For Numerical Optimization. Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department (2005)Google Scholar
  12. 12.
    Basturk, B., Karaboga, D.: An Artificial Bee Colony (ABC) Algorithm for Numeric function Optimization. In: IEEE Swarm Intelligence Symposium 2006, Indianapolis, Indiana, USA, May 12-14 (2006)Google Scholar
  13. 13.
    Goldberg, D.E., Deb, K.: A comparison of selection schemes used in genetic algorithms. In: Rawlins, G.J.E. (ed.) Foundations of Genetic Algorithms, pp. 69–93 (1991)Google Scholar
  14. 14.
    Storn, R., Price, K.: Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11, 341–359 (1997)zbMATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: 1995 IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)Google Scholar
  16. 16.
    Zavala, A.E.M., Aguirre, A.H., Diharce, E.R.V.: Constrained optimization via particle evolutionary swarm optimization algorithm (PESO). In: Proceedings of the 2005 conference on Genetic and evolutionary computation (GECCO’05), pp. 209–216 (2005)Google Scholar
  17. 17.
    Corne, D., Dorigo, M., Glover, F. (eds.): New Ideas in Optimization. McGraw-Hill, New York (1999)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Dervis Karaboga
    • 1
  • Bahriye Basturk
    • 1
  1. 1.Erciyes University, Engineering Faculty, The Department of Computer Engineering 

Personalised recommendations