The Analysis of Peculiar Control Parameters of Artificial Bee Colony Algorithm on the Numerical Optimization Problems


Artificial bee colony (ABC) algorithm is one of the popular swarm intelligence algorithms. ABC has been developed by being inspired foraging and waggle dance behaviors of real bee colonies in 2005. Since its invention in 2005, many ABC models have been proposed in order to solve different optimization problems. In all the models proposed, there are only one scout bee and a constant limit value used as control parameters for the bee population. In this study, the performance of ABC algorithm on the numeric optimization problems was analyzed by using different number of scout bees and limit values. Experimental results show that the results obtained by using more than one scout bee and different limit values, are better than the results of basic ABC. Therefore, the control parameters of the basic ABC should be tuned according to given class of optimization problems. In this paper, we propose reasonable value ranges of control parameters for the basic ABC in order to obtain better results on the numeric optimization problems.

Share and Cite:

Kiran, M. and Gündüz, M. (2014) The Analysis of Peculiar Control Parameters of Artificial Bee Colony Algorithm on the Numerical Optimization Problems. Journal of Computer and Communications, 2, 127-136. doi: 10.4236/jcc.2014.24017.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Karaboga, D. (2005) An Idea Based on Honey Bee Swarm for Numerical Optimization. Technical Report-TR06, Erciyes University.
[2] Frisch, K.V. (1992) Decoding Language of the Bee. Nobel Lectures. In: Lindsten, J., Ed., Physiology or Medicine 1971-1980, World Scientific Publishing, Singapore.
[3] Karaboga, D. and Basturk, B. (2008) On the Performance of Artificial Bee Colony (ABC) Algorithm. Applied Soft Computing, 8, 687-697.
[4] Akay, B. (2009) Performance Analysis of Artificial Bee Colony Algorithm on Numerical Optimization Problems. Ph.D. Thesis, Erciyes University, Graduate School of Natural and Applied Sciences, Kayseri, 70-72.
[5] Karaboga, D. and Basturk, B. (2007) A Po-werful and Efficient Algorithm for Numerical Function Optimization: Artificial Bee Colony (ABC) Algorithm. Journal of Global Optimization, 39, 459-471.
[6] Karaboga, N. (2009) A New Design Method Based on Artificial Bee Colony Algorithm for Digital IIR Filters. Journal of The Franklin Institute, 346, 328-348.
[7] Karaboga, D. and Akay, B. (2009) A Comparative Study of Artificial Bee Colony Algorithm. Applied Mathematics and Computation, 214, 108-132.
[8] Tsai, P.-W., Pan, J.-S., Liao, B.-Y. and Chu, S.-C. (2009) Enhanced Artificial Bee Colony Optimization. International Journal of Innovative Computing, 5, 5081-5092.
[9] Singh, A. (2009) An Artificial Bee Colony Algorithm for the Leaf Constrained Minimum Spanning Tree Problem. Applied Soft Computing, 9, 625-631.
[10] Alatas, B. (2010) Chaotic Bee Colony Algorithms for Global Numerical Optimization. Expert Systems with Applications, 37, 5682-5687.
[11] Zhu, G. and Kwong, S. (2010) Gbest-Guided Artificial Bee Colony Algorithm for Numerical Function Optimization. Applied Mathematics and Computation, 217, 3166-3173.
[12] Akay, B. and Karaboga, D. (2010) A Modified Artificial Bee Colony Algorithm for Real Parameter Optimization. In- formation Sciences.
[13] Zhang, C., Ouyang, D. and Ning, J. (2010) An Artificial Bee Colony Approach for Clustering. Expert Systems with Applications, 37, 4761-4767.
[14] Tasgetiren, M.F., Pan, Q.-K., Suganthan, P.N. and Chen, A.H-.L. (2011) A Discrete Artificial Bee Colony Algorithm for the Total Flowtime Minimization in Permutation Flow Shops. Information Sciences, 181, 3459-3475.
[15] Horng, M-.H. (2011) Multilevel Thresholding Selection Based on the Artificial Bee Colony Algorithm for Image Segmentation. Expert Systems with Applications.
[16] Ma, M., Lieang, J., Guo, M., Fan, Y. and Yin, Y. (2011) SAR Image Segmentation Based on Artificial Bee Colony Algorithm. Applied Soft Computing.
[17] Manoj, V.J. and Elias, E. (2011) Artificial Bee Colony Algorithm for the Design of Multiplier-Less Nonuniform Filter Bank Transmultiplexer. Information Sciences.
[18] De Oliveira, I.M.S. and Schirru, R. (2011) Swarm Intelligence of Artificial Bees Applied to In-Core Fuel Management Optimization. Annals of Nuclear Energy, 38, 1039-1045.
[19] Samanta, S. and Chakraborty, S. (2011) Parametric Optimization of Some Non-Traditional Machining Processes Using Artificial Bee Colony Algorithm. Engineering Applications of Artificial Intelligence.
[20] Yeh, W.-C. and Hsieh, T.-J. (2011) Solving Reliability Redundancy Allocation Problems Using an Artificial Bee Colony Algorithm. Computers & Operations Research, 38, 1465-1473.
[21] Sonmez, M. (2011) Artificial Bee Colony Algorithm for Optimization of Truss Structures. Applied Soft Computing, 11, 2406-2418.
[22] Gozde, H. and Taplamacioglu, M.C. (2011) Comparative Performance Analysis of Artificial Bee Colony Algorithms for Automatic Voltage Regulator (AVR) Systems. Journal of The Franklin Institute.
[23] Szeto, W.Y., Wu, Y. and Ho, S.C. (.2011) An Artificial Bee Colony Algorithm for the Capacitated Vehicle Routing Problem. European Journal of Operational Research.
[24] Seeley, T.D. (1995) The Wisdom of the Hive. Harvard University Press, Cambridge.
[25] Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y.-P., Auger, A. and Tiwari, S. (2005) Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real Parameter Optimization. Technical Report, Nanyang Technological University, Singapore.
[26] Boyer, D.O., Martinez, C.H. and Pedrajas, N.G. (2005) Crossover Operator for Evolutionary Algorithms Based on Population Features. Journal of Artificial Intelligence Research, 24, 1-48.
[27] De Jong, D. (1975) An Analysis of the Behavior of a Class of Genetic Adaptive Systems. Ph.D. Thesis, Department of Computer and Communication Sciences, University of Michigan, Ann Arbor.
[28] Rastrigin, L.A. (1974) Extremal Control Systems. In Theoretical Foundations of Engineering Cybernetics Series, Moscow.
[29] Griewank, A.O. (1981) Generalized Descent for Global Optimization. Journal of Optimization Theory and Applications, 34, 11-39.
[30] Ackley, D.H. (1987) An Empirical Study of Bit Vector Function Optimization. In: Davis, L., Ed., Genetic Algorithms and Simulated Annealing, Morgan Kaufmann, Los Altos, 171-204.
[31] B?ck, T. and Schwefel, H.P. (1993) An Overview of Evolutionary Algorithms for Parameter Optimization. Evolution Computing, 1, 1-23.
[32] Rosenbrock, H.H. (1960) An Automatic Method for Finding the Greatest or Least Value of a Function. Computer Journal, 3, 175-184.
[33] Shang, Y.W. and Qiu, Y.H. (2006) A Note on the Extended Rosenbrock Function. Evolution Computing, 14, 119-126.

Copyright © 2021 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.