CFP last date
16 December 2024
Call for Paper
January Edition
IJAIS solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 16 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Ordered Frequent Itemsets Matrix Based On Fp-Tree Structure And Apriori Algorithm

Abdulkader M. Al-Badani Abdualmajed A. Al-Khulaidi
Random Articles
Reseach Article

On the Performance of Explorative Artificial Bee Colony Algorithm for Numeric Function Optimization

by Tanveer Ahmed Belal, Md. Shahriar Rahman, Mohammad Shafiul Alam
journal cover thumbnail

International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 9 - Number 5
Year of Publication: 2015
Authors: Tanveer Ahmed Belal, Md. Shahriar Rahman, Mohammad Shafiul Alam
10.5120/ijais2015451413

Tanveer Ahmed Belal, Md. Shahriar Rahman, Mohammad Shafiul Alam . On the Performance of Explorative Artificial Bee Colony Algorithm for Numeric Function Optimization. International Journal of Applied Information Systems. 9, 5 ( August 2015), 24-30. DOI=10.5120/ijais2015451413

@article{ 10.5120/ijais2015451413,
author = { Tanveer Ahmed Belal, Md. Shahriar Rahman, Mohammad Shafiul Alam },
title = { On the Performance of Explorative Artificial Bee Colony Algorithm for Numeric Function Optimization },
journal = { International Journal of Applied Information Systems },
issue_date = { August 2015 },
volume = { 9 },
number = { 5 },
month = { August },
year = { 2015 },
issn = { 2249-0868 },
pages = { 24-30 },
numpages = {9},
url = { https://www.ijais.org/archives/volume9/number5/781-2015451413/ },
doi = { 10.5120/ijais2015451413 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:00:16.536880+05:30
%A Tanveer Ahmed Belal
%A Md. Shahriar Rahman
%A Mohammad Shafiul Alam
%T On the Performance of Explorative Artificial Bee Colony Algorithm for Numeric Function Optimization
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 9
%N 5
%P 24-30
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The Explorative Artificial Bee Colony (EABC) algorithm is a recently introduced swarm intelligence based algorithm that has been successfully tested to optimize only a limited number of multimodal functions. This paper evaluates EABC on a larger number of benchmark functions, including both unimodal and multimodal functions. EABC is an improved variant of the Artificial Bee Colony (ABC) algorithm. A major problem with the basic ABC algorithm is that it is more aligned towards exploitations, rather than explorations, which often leads to premature convergence and fitness stagnation. The improved variant — EABC tries to increase the degree of explorations of ABC by introducing more randomness during its perturbation operations. Besides, EABC customizes the degree of exploitations and explorations at the individual solution level, separately for each candidate solution of the bee population. EABC also introduces a crossover operation that assists the explorative perturbation operation of EABC. This paper extends the experimental studies on EABC by evaluating it on as many as 13 complex, high dimensional benchmark functions, including both unimodal and multimodal, separable and non-separable functions. The results are compared with the basic ABC algorithm. The comparison demonstrates that EABC often performs better optimization than the original ABC algorithm, which indicates the effectiveness of its more explorative operations.

References
  1. D. Karaboga and B. Basturk, On the performance of artificial bee colony (ABC) algorithm, Applied Soft Computing 8 (1) (2008) 687–697.
  2. D. Karaboga, An idea based on honey bee swarm for numerical optimization, Erciyes University, Kayseri, Turkey, Technical Report-TR06, 2005.
  3. D. Karaboga and B. Akay, A comparative study of artificial bee colony algorithm, Applied Mathematics and Computation 214 (1) (2009) 108–132.
  4. S. Sobti and P. Singla, Solving travelling salesman problem using bee colony based approach, International Journal of Engineering Research and Technology 2 (6) (2013) 186–189.
  5. K. Naidu, H. Mokhlis and A.H.A. Bakar, Multiobjective optimization using weighted sum Artificial Bee Colony algorithm for Load Frequency Control, International Journal of Electrical Power and Energy Systems 55 (2) (2014) 657–667.
  6. R. Mukherjee, D. Goswami and S. Chakraborty, Parametric optimization of Nd:YAG laser beam machining process using artificial bee colony algorithm, Journal of Industrial Engineering, vol. 2013, Article ID 570250, 15 pages, 2013. DOI: 10.1155/2013/570250.
  7. H. Garg, Solving structural engineering design optimization problems using an artificial bee colony algorithm, Journal of Industrial and Management Optimization, 10 (3) (2014) 777–794.
  8. Z. Zhao, D. Yin and Y. Jiang, Improved bee colony algorithm based on knowledge strategy for digital filter design, International Journal of Computer Applications, 47 (2) (2013) 241–248.
  9. A. Mishra, A. Khanna, N. Singh and V. Mishra, Speed control of DC motor using bee colony optimization, Universal Journal of Electrical and Electronic Engineering 1 (3) (2013) 68–75.
  10. A. Karegowda and M. Darshan, Optimizing feed forward neural network connection weights using artificial bee colony algorithm, International Journal of Advanced Research in Computer Science and Software Engineering 3 (7) (2013) 452–454.
  11. A. Bolaji, A. Khader, M. Betar and M. Awadallah, Bee colony algorithm, its variants and applications: A survey, Journal of Theoretical and Applied Technology 47 (2) (2013) 434–459.
  12. T. Park and K. R. Ryu, A Dual population genetic algorithm for adaptive diversity control, IEEE Trans. Evolutionary Computation 14 (6) (2010) 865–884.
  13. R. K. Ursem, Diversity guided evolutionary algorithms, in Proc. 7th Int. Conf. Parallel Problem Solving from Nature (PPSN), 2002, pp. 462–474.
  14. J. Lampinen and I. Zelinka, On stagnation of the differential evolution algorithm, in Proc. 6th Int. Mendel Conf. Soft Computing, Brno, Czech Republic, 2000, pp. 76–83.
  15. V. Tereshko, A. Loengarov, “Collective Decision-Making in Honey Bee Foraging Dynamics”, Comput. Inf. Sys. J., vol. 9, no. 3, pp. 1–7, 2005.
  16. M. Abd, A cooperative approach to the artificial bee colony algorithm, in Proc. IEEE Congress on Evolutionary Computation (CEC), 2010, pp. 1–5.
  17. W. Lee and 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 and S. Fan, Improved artificial bee colony algorithm with chaos, in Computer Science for Environmental Engineering and Eco-Informatics, Part I, Communications in Computer and Information Science, eds. Y. Yu, Z. Yu and J. Zhao, vol. 158, 2011, pp. 51–56.
  19. L. Fenglei, D. Haijun and F. Xing, The parameter improvement of bee colony algorithm in TSP problem, Science Paper Online, Nov. 2007.
  20. G. Zhu and S. Kwong, Gbest-guided artificial bee colony algorithm for numerical function optimization, Applied Mathematics and Computation 217 (7) (2010) 3166–3173.
  21. F. Kang, J. Li, Z. Ma and H. Li, Artificial bee colony algorithm with local search for numerical optimization, Journal of Software 6 (3) (2011) 490–497.
  22. E. Montes and R. Koeppel, Elitist artificial bee colony for constrained real-parameter optimization, in Proc. IEEE Congress on Evolutionary Computation, 2010, pp. 1–8.
  23. H. Quan and X. Shi, On the analysis of performance of the improved artificial bee colony algorithm, in Proc. 4th Int. Conf. Natural Computation (ICNC), 2008, pp. 654–658.
  24. F. Qingxian and D. Haijun, Bee colony algorithm for the function optimization, Science Paper Online, Aug. 2008.
  25. S. Kumar, V. Sharma and R. Kumari, A novel crossover based artificial bee colony algorithm for optimization, International Journal of Computer Applications 82 (8) (2013) 18–25.
  26. Y. Xu, P. Fan and L. Yuan, A simple and efficient artificial bee colony algorithm, Mathematical Problems in Engineering, vol. 2013, Article ID 526315, 9 pages, 2013. DOI: 10.1155/2013/526315.
  27. N. Sulaiman, J. Saleh and A. Abro, A modified artificial bee colony (JA-ABC) optimization algorithm, in Proc. International Conference on Applied Mathematics and Computational Methods in Engineering (AMCME), 2013, pp. 74–79.
  28. A. Abro and J. Saleh, Enhanced global-best artificial bee colony optimization algorithm, in Proc. 6th European Symposium on Computer Modeling and Simulation, 2012, pp. 95–100.
  29. W. Gao, S. Liu and L. Huang, A global best bee colony algorithm for global optimization, Journal of Computational and Applied Mathematics 236 (11) (2012) pp. 2741–2753.
  30. W. Gao and S. Liu, A modified artificial bee colony algorithm, Computers and Operations Research 39 (3) (2012) pp. 687–697.
  31. W. Gao and S. Liu, Improved artificial bee colony algorithm for global optimization, Information Processing Letters 111 (17) (2011) pp. 871–882.
  32. G. Zhu, S. Kwong, Gbest-guided bee colony algorithm for numerical optimization, Applied Mathematics and Computation 217 (7) (2010) pp. 3166–3173.
  33. A. Abro and J. Saleh, An enhanced artificial bee colony optimization algorithm, Recent Advances in Systems Science and Mathematical Modelling, ed. D.S. Nikos Mastorakis, Valeriu Prepelita, 2012: WSEAS Press.
  34. A. Banharnsakun, T. Achalakul and B. Sirinaovakul, The best-so-far selection in artificial bee colony algorithm, Applied Soft Computing 11 (2) (2011) pp. 2888–2901.
  35. S. S. Shapla, H. M. Haque and M. S. Alam, Explorative Artificial Bee Colony Algorithm: A Novel Swarm Intelligence Based Algorithm For Continuous Function Optimization, Accepted for publication in International Journal of Science and Research (IJSR) 4 (7) (2015).
  36. S. S. Hasan and F. Ahmed, Balaning Explorations with Exploitations in the Artificial Bee Colony Algorithm for Numerical Function Optimization, International Journal of Applied Information Systems 9 (1) (2015) pp. 42-48.
Index Terms

Computer Science
Information Sciences

Keywords

Explorative artificial bee colony algorithm; Exploration and exploitation; Continuous function optimization

Powered by PhDFocusTM