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Artificial Bee Colony Algorithm Improved with Evolutionary Operators
DOI:
https://doi.org/10.24215/16666038.18.e13Keywords:
ABC algorithm, parameter tuning, recombinationAbstract
In this paper, we design, implement, and analysis the replacement of the method to create new solutions in artificial bee colony algorithm by recombination operators, since the original method is similar to the recombination process used in evolutionary algorithms. For that purpose, we present a systematic investigation of the effect of using six different recombination operators for real-coded representations at the employed bee step. All the analysis is carried out using well known test problems. The experimental results suggest that the method to generate a new candidate food position plays an important role in the performance of the algorithm. Computational results and comparisons show that three of the six proposed algorithms are very competitive with the traditional bee colony algorithm.
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References
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[26] S. Z. Zhao, J. J. Liang, P. N. Suganthan, and M. F. Tasgetiren, “Dynamic multi-swarm particle swarm optimizer with local search for large scale global optimization,” in 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 3845–3852, June 2008.
[27] A. Zamuda, J. Brest, B. Boskovic, and V. Zumer, “Large scale global optimization using differential evolution with self-adaptation and cooperative co-evolution,” in 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 3718–3725, June 2008.
[28] S.-T. Hsieh, T.-Y. Sun, C.-C. Liu, and S.-J. Tsai, “Solving large scale global optimization using improved particle swarm optimizer,” in 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 1777–1784, June 2008.
[29] J. Brest, V. Zumer, and M. S. Maucec, “Selfadaptive differential evolution algorithm in constrained real-parameter optimization,” in 2006 IEEE International Conference on Evolutionary Computation, pp. 215–222, 2006.
[30] B. Boskovic, S. Greiner, J. Brest, and V. Zumer, “A differential evolution for the tuning of a chess evaluation function,” in 2006 IEEE International Conference on Evolutionary Computation, pp. 1851–1856, 2006.
[31] J. Brest and M. Sepesy Mauˇcec, “Population size reduction for the differential evolution algorithm,” Applied Intelligence, vol. 29, pp. 228–247, Dec 2008.
[2] S. Kockanat and N. Karaboga, “Parameter tuning of artificial bee colony algorithm for gaussian noise elimination on digital images,” in 2013 IEEE INISTA, pp. 1–4, 2013.
[3] B. Akay and D. Karaboga, “A modified artificial bee colony algorithm for real-parameter optimization,” Information Science, vol. 192, pp. 120–142, 2012.
[4] G. Zhu and S. Kwong, “Gbest-guided artificial bee colony algorithm for numerical function optimization,” AppliedMathematics and Computation, vol. 217, no. 7, pp. 3166 – 3173, 2010.
[5] K. Diwold, A. Aderhold, A. Scheidler, and M.Middendorf, “Performance evaluation of artificial bee colony optimization and new selection schemes,” Memetic Computing, vol. 3, no. 3, p. 149, 2011.
[6] A. Banharnsakun, T. Achalakul, and B. Sirinaovakul, “The best-so-far selection in artificial bee colony algorithm,” Applied Soft Computing, vol. 11, no. 2, pp. 2888 – 2901, 2011. The Impact of Soft Computing for the Progress of Artificial Intelligence.
[7] B. Alatas, “Chaotic bee colony algorithms for global numerical optimization,” Expert Systems with Applications, vol. 37, no. 8, pp. 5682 –5687, 2010.
[8] W. Gao, S. Liu, and L. Huang, “A global best artificial bee colony algorithm for global optimization,” Journal of Computational and Applied Mathematics, vol. 236, no. 11, pp. 2741– 2753, 2012.
[9] W. feng Gao and S. yang Liu, “A modified artificial bee colony algorithm,” Computers & Operations Research, vol. 39, no. 3, pp. 687 – 697, 2012.
[10] S. S. Jadon, R. Tiwari, H. Sharma, and J. C. Bansal, “Hybrid Artificial Bee Colony algorithm with Differential Evolution,” Applied Soft Computing, vol. 58, pp. 11 – 24, 2017.
[11] X. Yan, Y. Zhu, and W. Zou, “A hybrid artificial bee colony algorithm for numerical function optimization,” in 2011 11th International Conference on Hybrid Intelligent Systems (HIS), pp. 127–132, Dec 2011.
[12] L. Li, F. Zhang, C. Liu, and B. Niu, “A hybrid Artificial Bee Colony algorithm with bacterial foraging optimization,” in 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), pp. 127–132, 2015.
[13] K. Tang, X. Yao, P. N. Suganthan, C. Mac-Nish, Y. P. Chen, C. M. Chen, , and Z. Yang, “Benchmark functions for the CEC’2008 special session and competition on large scale global optimization,” technical report, Nature Inspired Computation and Applications Laboratory, USTC, China, 2007.
[14] G. F. Minetti and C. Salto, “Improving artificial bee colony algorithm with evolutionary operators,” in XXIII Congreso Argentino de Ciencias de la Computación (CACIC 2017)., pp. 93–102, 2017.
[15] M.-D. Zhang, Z.-H. Zhan, J.-J. Li, and J. Zhang, Tournament Selection Based Artificial Bee Colony Algorithm with Elitist Strategy, pp. 387–396. Cham: Springer International Publishing, 2014.
[16] F. Herrera, M. Lozano, and A. Sánchez, “A taxonomy for the crossover operator for realcoded genetic algorithms: An experimental study,” International Journal of Intelligent Systems, vol. 18, no. 3, pp. 309—-338, 2003.
[17] A. H. Wright, “Genetic algorithms for real parameter optimization,” in Foundations of Genetic Algorithms, pp. 205–218, Morgan Kaufmann, 1991.
[18] G. Syswerda, “Uniform crossover in genetic algorithms,” in Proceedings of the 3rd International Conference on Genetic Algorithms, pp. 2–9, Morgan Kaufmann Publishers Inc., 1989.
[19] L. J. Eshelman, R. A. Caruana, and J. D. Schaffer, “Biases in the crossover landscape,” in Proceedings of the Third International Conference on Genetic Algorithms, pp. 10–19,Morgan Kaufmann Publishers Inc., 1989.
[20] D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (abc) algorithm,” J. of Global Optimization, vol. 39, pp. 459–471, Nov. 2007.
[21] X. Y., F. P., and Y. L., “A simple and efficient artificial bee colony algorithm,” Mathematical Problems in Engineering, vol. 2013, 2013.
[22] R. A. and P. M., “An improved self-adaptive artificial bee colony algorithm for global optimisation,” International Journal of Swarm Intelligence (IJSI), vol. 1, no. 2, 2014.
[23] L.-Y. Tseng and C. Chen, “Multiple trajectory search for large scale global optimization,” in 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 3052–3059, June 2008.
[24] J. Brest, A. Zamuda, B. Boskovic, M. S. Maucec, and V. Zumer, “High-dimensional realparameter optimization using self-adaptive differential evolution algorithm with population size reduction,” in 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 2032–2039, June 2008.
[25] Z. Yang, K. Tang, and X. Yao, “Multilevel cooperative coevolution for large scale optimization,” in 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 1663–1670, June 2008.
[26] S. Z. Zhao, J. J. Liang, P. N. Suganthan, and M. F. Tasgetiren, “Dynamic multi-swarm particle swarm optimizer with local search for large scale global optimization,” in 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 3845–3852, June 2008.
[27] A. Zamuda, J. Brest, B. Boskovic, and V. Zumer, “Large scale global optimization using differential evolution with self-adaptation and cooperative co-evolution,” in 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 3718–3725, June 2008.
[28] S.-T. Hsieh, T.-Y. Sun, C.-C. Liu, and S.-J. Tsai, “Solving large scale global optimization using improved particle swarm optimizer,” in 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 1777–1784, June 2008.
[29] J. Brest, V. Zumer, and M. S. Maucec, “Selfadaptive differential evolution algorithm in constrained real-parameter optimization,” in 2006 IEEE International Conference on Evolutionary Computation, pp. 215–222, 2006.
[30] B. Boskovic, S. Greiner, J. Brest, and V. Zumer, “A differential evolution for the tuning of a chess evaluation function,” in 2006 IEEE International Conference on Evolutionary Computation, pp. 1851–1856, 2006.
[31] J. Brest and M. Sepesy Mauˇcec, “Population size reduction for the differential evolution algorithm,” Applied Intelligence, vol. 29, pp. 228–247, Dec 2008.
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Published
2018-10-04
How to Cite
Minetti, G., & Salto, C. (2018). Artificial Bee Colony Algorithm Improved with Evolutionary Operators. Journal of Computer Science and Technology, 18(02), e13. https://doi.org/10.24215/16666038.18.e13
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