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.
This is a preview of subscription content, access via your institution.
Buying options
Preview
Unable to display preview. Download preview PDF.
References
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)
Hedar, A.R., Fukushima, M.: Derivative-Free Filter Simulated Annealing Method for Constrained Continuous Global Optimization
Michalewicz, Z., Schoenauer, M.: Evolutionary Algorithms for Constrained Parameter Optimization Problems. Evolutionary Computation 4(1), 1–32 (1995)
Floudas, C.A., Pardalos, P.M. (eds.): A Collection of Test Problems for Constrained Global Optimization Algorithms. LNCS, vol. 455. Springer, Heidelberg (1990)
Himmelblau, D.M.: Applied Nonlinear Programming. McGraw-Hill, New York (1972)
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)
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)
Hu, X., Eberhart, R.C., Shi, Y.H.: Engineering optimization with particle swarm. In: IEEE Swarm Intelligence Symposium, pp. 53–57 (2003)
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)
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)
Karaboga, D.: An Idea Based On Honey Bee Swarm For Numerical Optimization. Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department (2005)
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)
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)
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)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: 1995 IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
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)
Corne, D., Dorigo, M., Glover, F. (eds.): New Ideas in Optimization. McGraw-Hill, New York (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Karaboga, D., Basturk, B. (2007). Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems. In: Melin, P., Castillo, O., Aguilar, L.T., Kacprzyk, J., Pedrycz, W. (eds) Foundations of Fuzzy Logic and Soft Computing. IFSA 2007. Lecture Notes in Computer Science(), vol 4529. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72950-1_77
Download citation
DOI: https://doi.org/10.1007/978-3-540-72950-1_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72917-4
Online ISBN: 978-3-540-72950-1
eBook Packages: Computer ScienceComputer Science (R0)