Knowledge-Based Artificial Bee Colony Algorithm for Optimization Problems
Keywords:
Artificial Bee Colony; Cultural Algorithm; Cultural Artificial Bee Colony Algorithm; Exploration and Exploitation
Abstract
This paper presents a cultural algorithm-based artificial bee colony algorithm to modify the artificial bee colony (ABC). The
normative and situational knowledge inherent in the cultural algorithm is utilized to guide the step size as well as the
direction of evolution of ABC at different arrangements. This was done in order to combat the disparity between exploration
and exploitation associated with the standard ABC, which results in poor convergence and optimization inefficiency. Four
variants of Cultural Artificial Bee Colony Algorithm (CABCA) are accomplished in MATLAB/Simulink program using different
configurations of cultural knowledge. A total of 20 standards applied mathematical optimization benchmark functions
(Ackley, Michalewicz, Quartic, Sphere etc) are employed to evaluate the performance, and it was found that all the four
variants of CABCA outperformed the standard ABC. The superiority of CABCA variants over ABC justifies the essence of
knowledge introduction in the belief space for self-adaptation.
References
Adebiyi, B. H., Tekanyi, A. M. S., and Salawudeen, T. A. (2017). An Improved Artificial Bee Colony using Cultural
Algorithm for Optimization Problem. International Journal of Computer Applications, 160(8).
Alam, M. S., Islam, M. M., and Murase, K. (2015). Artificial Bee Colony Algorithm with Adaptive Explorations and
Exploitations: A Novel Approach for Continuous Optimization. International Journal of Applied Information
System, 8(2), 32 - 43.
Banharnsakun, A., Achalakul, T., and Sirinaovakul, B. (2011). The best-so-far selection in artificial bee colony algorithm.
Applied soft computing, 11(2), 2888-2901.
Chung, C.-J. (1997). Knowledge-based approaches to self-adaptation in cultural algorithms.
Chung, C. (1997). Fuzzy Approaches to Acquiring Experimental Knowledge in Cultural Algorithms. Paper presented at
the Proceedings of the 9th International Conference on Tools with Artificial Intelligence.
El-Telbany, M. E. (2013). Tuning PID controller for DC motor: An artificial bees optimization approach. International
Journal of Computer Applications, 77(15).
Gao, W.-f., Huang, L.-l., Wang, J., Liu, S.-y., and Qin, C.-d. (2016). Enhanced artificial bee colony algorithm through
differential evolution. Applied soft computing, 48, 137-150.
Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization: Technical report-tr06, Erciyes
university, engineering faculty, computer engineering department.
Karaboga, D., and Akay, B. (2009). A comparative study of artificial bee colony algorithm. Applied Mathematics and
Computation, 214(1), 108-132.
Karaboga, D., and Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial
bee colony (ABC) algorithm. Journal of global optimization, 39(3), 459-471.
Karaboga, D., and Basturk, B. (2008). On the performance of artificial bee colony (ABC) algorithm. Applied soft
computing, 8(1), 687-697.
Lee, W.-P., and Cai, W.-T. (2011). A novel artificial bee colony algorithm with diversity strategy. Paper presented at the
Natural Computation (ICNC), 2011 Seventh International Conference on.
Liang, Z., Hu, K., Zhu, Q., and Zhu, Z. (2017). An enhanced artificial bee colony algorithm with adaptive differential
operators. Applied soft computing, 58, 480-494.
Lin, C.-J., and Su, S.-C. (2012). Using an efficient artificial bee colony algorithm for protein structure prediction on
lattice models. International journal of innovative computing, information and control, 8, 2049-2064.
Priti Bansal, S. S., and Nitish Mittal. (2017). A Hybrid Artificial Bee Colony and Harmony Search Algorithm to Generate
Covering Arrays for Pair-wise Testing. International Journal of Intelligent Systems andApplications(IJISA), 9(8),
59-70. doi: 10.5815.
Reynolds, R. G., and Chung, C. (1997). Fuzzy approaches to acquiring experimental knowledge in cultural algorithms.
Paper presented at the Tools with Artificial Intelligence, 1997. Proceedings., Ninth IEEE International
Conference on.
Reynolds, R. G., and Peng, B. (2004). Cultural algorithms: modeling of how cultures learn to solve problems. Paper
presented at the Tools with Artificial Intelligence, 2004. ICTAI 2004. 16th IEEE International Conference on.
Reynolds, R. G., and Peng, B. (2005). Cultural algorithms: computational modeling of how cultures learn to solve
problems: an engineering example. Cybernetics and Systems: An International Journal, 36(8), 753-771.
Salawudeen, A. T. (2015). Development of an Improved Cultural Artificial Fish Swarm Algorithm with Crossover. ( M.Sc
Dissertation). Department of Electrical and Computer Engineering, Ahmadu Bello University Zaria, Nigeria.
p.154.
Yan, G., and Li, C. (2011). An effective refinement artificial bee colony optimization algorithm based on chaotic search
and application for pid control tuning. Journal of Computational Information Systems, 7(9), 3309-3316.
Algorithm for Optimization Problem. International Journal of Computer Applications, 160(8).
Alam, M. S., Islam, M. M., and Murase, K. (2015). Artificial Bee Colony Algorithm with Adaptive Explorations and
Exploitations: A Novel Approach for Continuous Optimization. International Journal of Applied Information
System, 8(2), 32 - 43.
Banharnsakun, A., Achalakul, T., and Sirinaovakul, B. (2011). The best-so-far selection in artificial bee colony algorithm.
Applied soft computing, 11(2), 2888-2901.
Chung, C.-J. (1997). Knowledge-based approaches to self-adaptation in cultural algorithms.
Chung, C. (1997). Fuzzy Approaches to Acquiring Experimental Knowledge in Cultural Algorithms. Paper presented at
the Proceedings of the 9th International Conference on Tools with Artificial Intelligence.
El-Telbany, M. E. (2013). Tuning PID controller for DC motor: An artificial bees optimization approach. International
Journal of Computer Applications, 77(15).
Gao, W.-f., Huang, L.-l., Wang, J., Liu, S.-y., and Qin, C.-d. (2016). Enhanced artificial bee colony algorithm through
differential evolution. Applied soft computing, 48, 137-150.
Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization: Technical report-tr06, Erciyes
university, engineering faculty, computer engineering department.
Karaboga, D., and Akay, B. (2009). A comparative study of artificial bee colony algorithm. Applied Mathematics and
Computation, 214(1), 108-132.
Karaboga, D., and Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial
bee colony (ABC) algorithm. Journal of global optimization, 39(3), 459-471.
Karaboga, D., and Basturk, B. (2008). On the performance of artificial bee colony (ABC) algorithm. Applied soft
computing, 8(1), 687-697.
Lee, W.-P., and Cai, W.-T. (2011). A novel artificial bee colony algorithm with diversity strategy. Paper presented at the
Natural Computation (ICNC), 2011 Seventh International Conference on.
Liang, Z., Hu, K., Zhu, Q., and Zhu, Z. (2017). An enhanced artificial bee colony algorithm with adaptive differential
operators. Applied soft computing, 58, 480-494.
Lin, C.-J., and Su, S.-C. (2012). Using an efficient artificial bee colony algorithm for protein structure prediction on
lattice models. International journal of innovative computing, information and control, 8, 2049-2064.
Priti Bansal, S. S., and Nitish Mittal. (2017). A Hybrid Artificial Bee Colony and Harmony Search Algorithm to Generate
Covering Arrays for Pair-wise Testing. International Journal of Intelligent Systems andApplications(IJISA), 9(8),
59-70. doi: 10.5815.
Reynolds, R. G., and Chung, C. (1997). Fuzzy approaches to acquiring experimental knowledge in cultural algorithms.
Paper presented at the Tools with Artificial Intelligence, 1997. Proceedings., Ninth IEEE International
Conference on.
Reynolds, R. G., and Peng, B. (2004). Cultural algorithms: modeling of how cultures learn to solve problems. Paper
presented at the Tools with Artificial Intelligence, 2004. ICTAI 2004. 16th IEEE International Conference on.
Reynolds, R. G., and Peng, B. (2005). Cultural algorithms: computational modeling of how cultures learn to solve
problems: an engineering example. Cybernetics and Systems: An International Journal, 36(8), 753-771.
Salawudeen, A. T. (2015). Development of an Improved Cultural Artificial Fish Swarm Algorithm with Crossover. ( M.Sc
Dissertation). Department of Electrical and Computer Engineering, Ahmadu Bello University Zaria, Nigeria.
p.154.
Yan, G., and Li, C. (2011). An effective refinement artificial bee colony optimization algorithm based on chaotic search
and application for pid control tuning. Journal of Computational Information Systems, 7(9), 3309-3316.
Published
2020-02-22
How to Cite
Adebiyi, B. H., Mu’azu, M. B., Tekanyi, A. M. S., Salawudeen, A. T., & Adebiyi, R. F. (2020). Knowledge-Based Artificial Bee Colony Algorithm for Optimization Problems. Journal of Engineering Research, 23(1), 13-25. Retrieved from http://jer.unilag.edu.ng/article/view/589
Section
Articles
