Artificial bee colony algorithm variants on constrained optimization
DOI:
https://doi.org/10.11121/ijocta.01.2017.00342Keywords:
Artificial Bee Colony Algorithm, Constrained Optimization, Deb's rulesAbstract
Optimization problems are generally classified into two main groups:unconstrained and constrained. In the case of constrainedoptimization, special techniques are required to handle withconstraints and produce solutions in the feasible space. Intelligentoptimization techniques that do not make assumptions on the problemcharacteristics are preferred to produce acceptable solutions to theconstrained optimization problems. In this study, the performance ofartificial bee colony algorithm (ABC), one of the intelligentoptimization techniques, is investigated on constrained problems andthe effect of some modifications on the performance of the algorithmis examined. Different variants of the algorithm have been proposedand compared in terms of efficiency and stability. Depending on theresults, when DE operators were integrated into ABC algorithm'sonlooker phase while the employed bee phase is retained as in ABCalgorithm, an improvement in the performance was gained in terms ofthe best solution in addition to preserving the stability of thebasic ABC. The ABC algorithm is a simple optimization algorithm thatcan be used for constrained optimization without requiring a prioriknowledge.Downloads
References
David E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1st edition, 1989.
Carlos A. Coello Coello. A survey of constraint handling techniques used with evolutionary algorithms. Technical report, Laboratorio Nacional de Informtica Avanzada, 1999.
John H. Holland. Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, 1975.
R. Storn and K. Price. Tr-95-01: Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical report, Berkeley, CA,, 1995.
Marco Dorigo, V. Maniezzo, Alberto Colorni, Marco Dorigo, Marco Dorigo, Vittorio Maniezzo, Vittorio Maniezzo, Alberto Colorni, and Alberto Colorni. Tr 91-016: Positive feedback as a search strategy. Technical report, Politecnico di Milano, Italy, 1991.
J. Kennedy and R. C. Eberhart. Particle swarm optimization. In 1995 IEEE International Conference on NeuralCrossref
D. Karaboga. An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Erciyes
S. Koziel and Z. Michalewicz. Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization. Evol. Comput., 7(1):19–44, 1999.Crossref
Dervis Karaboga and Bahriye Basturk. Foundations of Fuzzy Logic and Soft Computing: 12th International Fuzzy Systems Association World Congress, IFSA 2007, Cancun, Mexico, June 18-21, 2007. Proceedings, chapter Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems, pages 789–798. Springer Berlin Heidelberg, Berlin, Heidelberg, 2007.
Dervis Karaboga and Bahriye Akay. A modified artificial bee colony (abc) algorithm for constrained optimization problems. Applied Soft Computing, 11(3):3021 – 3031, 2011.
https://doi.org/10.1016/j.asoc.2010.12.001">Crossref
K. Deb. An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics
D. Karaboga and B. Akay. A survey: Algorithms simulating bee swarm intelligence. Artificial Intelligence Review,Crossref
Dervis Karaboga and Beyza Gorkemli. A quick artificial bee colony (qabc) algorithm and its performance on optimization problems. Applied Soft Computing, 23:227 – 238, 2014.Crossref
Bahriye Akay and Dervis Karaboga. A survey on the applications of artificial bee colony in signal, image, and video processing. Signal, Image and Video Processing, 9(4):967–990, 2015.Crossref
D. Karaboga and B. Basturk. On the performance of artificial bee colony (abc) algorithm. Applied Soft Computing, 8(1):687–697, 2008.Crossref
Z. Michalewicz and M. Schoenauer. Evolutionary algorithms for constrained parameter optimization problems. Evolutionary Computation, 4(1):1– 32, 1995.Crossref
E. Mezura-Montes and C.A. Coello Coello. A Simple Multimembered Evolution Strategy to Solve Constrained Optimization Problems. Technical Report EVOCINV-04-2003, Evolutionary Computation Group at CINVESTAV, Secci´on de Computaci´on, Departamento de Ingenier´ıa El´ectrica, CINVESTAV-IPN, M´exico D.F., M´exico, 2003. Available in the Constraint Handling Techniques in Evolutionary Algorithms Repository at http://www.cs.cinvestav.mx/˜constraint/.
Downloads
Published
How to Cite
Issue
Section
License
Articles published in IJOCTA are made freely available online immediately upon publication, without subscription barriers to access. All articles published in this journal are licensed under the Creative Commons Attribution 4.0 International License (click here to read the full-text legal code). This broad license was developed to facilitate open access to, and free use of, original works of all types. Applying this standard license to your work will ensure your right to make your work freely and openly available.
Under the Creative Commons Attribution 4.0 International License, authors retain ownership of the copyright for their article, but authors allow anyone to download, reuse, reprint, modify, distribute, and/or copy articles in IJOCTA, so long as the original authors and source are credited.
The readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material
- for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
under the following terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
This work is licensed under a Creative Commons Attribution 4.0 International License.
