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Artificial Bee Colony Algorithm with Adaptive Explorations and Exploitations: A Novel Approach for Continuous Optimization

by Mohammad Shafiul Alam, Md. Monirul Islam, Kazuyuki Murase
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 8 - Number 2
Year of Publication: 2015
Authors: Mohammad Shafiul Alam, Md. Monirul Islam, Kazuyuki Murase
10.5120/ijais15-451286

Mohammad Shafiul Alam, Md. Monirul Islam, Kazuyuki Murase . Artificial Bee Colony Algorithm with Adaptive Explorations and Exploitations: A Novel Approach for Continuous Optimization. International Journal of Applied Information Systems. 8, 2 ( January 2015), 32-43. DOI=10.5120/ijais15-451286

@article{ 10.5120/ijais15-451286,
author = { Mohammad Shafiul Alam, Md. Monirul Islam, Kazuyuki Murase },
title = { Artificial Bee Colony Algorithm with Adaptive Explorations and Exploitations: A Novel Approach for Continuous Optimization },
journal = { International Journal of Applied Information Systems },
issue_date = { January 2015 },
volume = { 8 },
number = { 2 },
month = { January },
year = { 2015 },
issn = { 2249-0868 },
pages = { 32-43 },
numpages = {9},
url = { https://www.ijais.org/archives/volume8/number2/710-1286/ },
doi = { 10.5120/ijais15-451286 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T18:58:44.558688+05:30
%A Mohammad Shafiul Alam
%A Md. Monirul Islam
%A Kazuyuki Murase
%T Artificial Bee Colony Algorithm with Adaptive Explorations and Exploitations: A Novel Approach for Continuous Optimization
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 8
%N 2
%P 32-43
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A proper balance between global explorations and local exploitations is often considered necessary for complex, high dimensional optimization problems to avoid local optima and to find a good near optimum solution with sufficient convergence speed. This paper introduces Artificial Bee Colony algorithm with Adaptive eXplorations and eXploitations (ABC-AX2), a novel algorithm that improves over the basic Artificial Bee Colony (ABC) algorithm. ABC AX2 augments each candidate solution with three control parameters that control the perturbation rate, magnitude of perturbations and proportion of explorative and exploitative perturbations. Together, all the control parameters try to adapt the degree of global explorations and local exploitations around each candidate solution by affecting how new trial solutions are produced from the existing ones. The control parameters are automatically adapted at the individual solution level, separately for each candidate solution. ABC AX2 is tested on a number of benchmark problems of continuous optimization and compared with the basic ABC algorithm and several other recent variants of ABC algorithm. Results show that the performance of ABC AX2 is often better than most other algorithms in comparison, in terms of both convergence speed and final solution quality.

References
  1. D. Karaboga, "An idea based on honey bee swarm for numerical optimization", Erciyes University, Kayseri, Turkey, Technical Report-TR06, 2005.
  2. D. Karaboga, B. Akay, "A comparative study of artificial bee colony algorithm", Applied Mathematics and Computation 214 (1) (2009) 108–132.
  3. Q. Bai, X. Yun, "A new hybrid artificial bee colony algorithm for the traveling salesman problem", in: Proc. 3rd Int. Conf. Communication Software and Networks (ICCSN), 2011, pp. 155–159.
  4. N. Stanarevic, M. Tuba, N. Bacanin, "Modified artificial bee colony algorithm for constrained problems optimization", Int. Journal of Mathematical Models and Methods in Applied Sciences 5 (3) (2011) 644–651.
  5. S. Omkar, J. Senthilnath, R. Khandelwal, G. Naik, S. Gopalakrishnan, "Artificial bee colony (ABC) for multi-objective design optimization of composite structures", Applied Soft Computing 11 (1) (2011) 489–499.
  6. F. Kang, J. Li, Q. Xu, "Structural inverse analysis by hybrid simplex artificial bee colony algorithms", Computers and Structures 87 (13–14) (2009) 861–870.
  7. R. Irani, R. Nasimi, "Application of artificial bee colony-based neural network in bottom hole pressure prediction in underbalanced drilling", Journal of Petroleum Science and Engineering 78 (1) (2011) 6–12.
  8. N. Karaboga, "A new design method based on artificial bee colony algorithm for digital IIR filters", Journal of the Franklin Institute 346 (4) (2009) 328–348.
  9. D. Karaboga, B. Akay, "PID controller design by using artificial bee colony, harmony search and bees algorithms", in: Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 224 (7) (2010) 869–883.
  10. R. Rao, P. Pawar, "Parameter optimization of a multi pass milling process using non-traditional optimization algorithms", Applied Soft Computing 10 (2) (2010) 445-456.
  11. D. Karaboga, B. Gorkemli, C. Ozturk, N. Karaboga, "A comprehensive survey: artificial bee colony (ABC) algorithm and applications", Artificial Intelligence Review (2012) 1–37.
  12. L. Bao, J. Zeng, "Comparison and analysis of the selection mechanism in the artificial bee colony algorithm", in: Proc. 9th Int. Conf. Hybrid Intelligent Systems, 2009, pp. 411–416.
  13. W. Gao, S. Liu, "A modified artificial bee colony algorithm", Computers and Operations Research 39 (3) (2012) 687–697.
  14. J. Lampinen, I. Zelinka, "On stagnation of the differential evolution algorithm", in: Proc. 6th Int. Mendel Conf. on Soft Computing, 2000, pp. 76–83.
  15. M. S. Alam, M. M. Islam, "Artificial bee colony algorithm with self-adaptive mutation: A novel approach for numeric optimization", in: Proc. 2011 IEEE Int. Conf. on Trends and Developments in Converging Technology (TENCON), 2011, pp. 49–53.
  16. M. Abd, "A cooperative approach to the artificial bee colony algorithm", in: IEEE Congress on Evolutionary Computation (CEC), 2010, pp. 1–5.
  17. W. Lee, W. Cai, "A novel artificial bee colony algorithm with diversity strategy", in: Proc. 7th Int. Conf. Natural Computation, 2011, pp. 1441–1444.
  18. B. Wu, S. Fan, "Improved Artificial Bee Colony Algorithm with Chaos", in: Y. Yu, Z. Yu, J. Zhao (Eds. ): Computer Science for Environmental Engineering and EcoInformatics, Part I, Communications in Computer and Information Science, vol. 158, 2011, pp. 51-56.
  19. L. Fenglei, D. Haijun, F. Xing, "The parameter improvement of bee colony algorithm in TSP problem", Science Paper Online, November 2007.
  20. G. Zhu, S. Kwong, "Gbest-guided artificial bee colony algorithm for numerical function optimization", Applied Mathematics & Computation 217 (7) (2010) 3166–3173.
  21. F. Kang, J. Li, Z. Ma, H. Li, "Artificial bee colony algorithm with local search for numerical optimization", Journal of Software 6 (3) (2011) 490–497.
  22. F. Qingxian, D. Haijun, "Bee colony algorithm for the function optimization", Science Paper Online, 2008.
  23. H. Quan, X. Shi, "On the analysis of performance of the improved ABC algorithm", in: 4th IEEE Int. Conf. Natural Computation (ICNC), 2008, pp. 654–658.
  24. E. Montes, R. Koeppel, "Elitist artificial bee colony for constrained real-parameter optimization", IEEE Congress on Evolutionary Computation 11 (2010) 1–8.
  25. S. Nieberg, H. Beyer, "Self-adaptation in evolutionary algorithms", Parameter Setting in Evolutionary Algorithm (2007) 47–76.
  26. J. Liang, A. Qin, P. Suganthan, S. Baskar, "Comprehensive learning particle swarm optimizer for global optimization of multimodal functions", IEEE Trans. on Evolutionary Comput. 10 (3) (2006) 281 295.
  27. C. Lee, X. Yao, "Evolutionary programming using mutations based on the Lévy probability distribution", IEEE Transactions on Evolutionary Computation 8 (1) (2004) 1–13.
  28. X. Yao, Y. Liu, G. Lin, "Evolutionary programming made faster", IEEE Transactions on Evolutionary Computation 3 (2) (1999) 82–102.
Index Terms

Computer Science
Information Sciences

Keywords

Artificial bee colony algorithm; Exploration and exploitation; Continuous optimization; Meta-heuristic optimization.

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