CFP last date
15 January 2025
Call for Paper
February Edition
IJAIS solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 15 January 2025

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Cybersecurity Through LSTM-Based Phishing URL Detection

T.J. Ayo O.D. Alowolodu D.P. Omotayo
Random Articles
Reseach Article

Adaptive Mutation Rate for the Artificial Bee Colony Algorithm: A Case Study on Benchmark Continuous Optimization Problems

by Syeda Shabnam Hasan
journal cover thumbnail

International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 9 - Number 4
Year of Publication: 2015
Authors: Syeda Shabnam Hasan
10.5120/ijais15-451397

Syeda Shabnam Hasan . Adaptive Mutation Rate for the Artificial Bee Colony Algorithm: A Case Study on Benchmark Continuous Optimization Problems. International Journal of Applied Information Systems. 9, 4 ( July 2015), 42-48. DOI=10.5120/ijais15-451397

@article{ 10.5120/ijais15-451397,
author = { Syeda Shabnam Hasan },
title = { Adaptive Mutation Rate for the Artificial Bee Colony Algorithm: A Case Study on Benchmark Continuous Optimization Problems },
journal = { International Journal of Applied Information Systems },
issue_date = { July 2015 },
volume = { 9 },
number = { 4 },
month = { July },
year = { 2015 },
issn = { 2249-0868 },
pages = { 42-48 },
numpages = {9},
url = { https://www.ijais.org/archives/volume9/number4/771-1397/ },
doi = { 10.5120/ijais15-451397 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:00:04.303411+05:30
%A Syeda Shabnam Hasan
%T Adaptive Mutation Rate for the Artificial Bee Colony Algorithm: A Case Study on Benchmark Continuous Optimization Problems
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 9
%N 4
%P 42-48
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A major problem with the Artificial Bee Colony (ABC) algorithm is its premature convergence to the locally optimal points of the search space, which often originates from the lack of explorative search capability of its mutation operator. This paper introduces ABC with Adaptive Mutation Rate (ABC-AMR), a novel algorithm that modifies the basic mutation operation of the original ABC algorithm in an explorative way. The novelty of the proposed algorithm lies in an adaptive mutation strategy that enables ABC-AMR to automatically adjust the mutation rate, separately for each candidate solution of the bee population, in order to customize the degree of explorations and exploitations around each candidate solution, while the original ABC algorithm employs a naïve fixed mutation rate. Besides, a few more explorative schemes and parameter values are employed by ABC-AMR to assist the adaptive mutation procedure. ABC-AMR is evaluated on several benchmark numerical optimization problems and results are compared with the basic ABC algorithm. Results show that ABC-AMR can perform better optimization than the original ABC algorithm on some of the benchmark problems.

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 arti?cial bee colony algorithm, in Proc. 4th Int. Conf. Natural Computation (ICNC), 2008, 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. 20 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 and S. Kwong, Gbest-guided artificial bee colony algorithm for numerical function 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. C. Lin and S. Su, Using an efficient bee colony algorithm for protein structure prediction, Int. Journal of Innovative Computing, Information and Control 8 (3b) (2012) 2049–2064.
  36. M. Abdulsalam and A. Bakar, A cluster based deviation detection using the artificial bee colony (ABC) algorithm, International Journal of Soft Computing 7 (2) (2012) 71–78.
  37. A. Ozen and C. Ozturk. "A novel modulation recognition technique based on artificial bee colony algorithm in the presence of multipath fading channels, in Proc. IEEE 36th International Conference on Telecommunications and Signal Processing (TSP), 2013, pp. 239–243.
  38. B. Akay and D. Karaboga, Artificial bee colony algorithm for large scale problems and design optimization, Journal of Intelligent Manufacturing 23 (4) (2012), 1001–1014.
Index Terms

Computer Science
Information Sciences

Keywords

Artificial bee colony algorithm; Mutation; Exploration and exploitation; Continuous optimization.

Powered by PhDFocusTM