A Novel Artificial Bee Colony Algorithm with an Overall-Degradation Strategy and Its Performance on the Benchmark Functions of CEC 2014 Special Session

Bai Li

doi:doi:10.11648/j.acis.20140205.11

| Peer-Reviewed

A Novel Artificial Bee Colony Algorithm with an Overall-Degradation Strategy and Its Performance on the Benchmark Functions of CEC 2014 Special Session

Bai Li

Published in Automation, Control and Intelligent Systems (Volume 2, Issue 5)

Received: 23 January 2014 Accepted: 4 September 2014 Published: 30 September 2014

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

The artificial bee colony (ABC) algorithm has been a well-known swarm intelligence algorithm, which assimilates the cooperating behavior of bees when seeking for nectar sources. Aiming to improve the conventional ABC algorithm, we focus on the re-initialization phase. In this paper, an overall-degradation-oriented artificial bee colony (OD-ABC) algorithm is proposed, pursuing to fight against premature convergence. This is achieved through re-initializing majority of the employed bees at one time, rather than generating at most one scout bee in each iteration. In this work, our OD-ABC algorithm is compared against the conventional ABC algorithms using 24 benchmark functions that origin from the CEC 2014’s competition on single objective real-parameter numerical optimization. The numerical results show that the OD-ABC algorithm is effective and thus can be employed to fight against premature convergence.

Published in	Automation, Control and Intelligent Systems (Volume 2, Issue 5)
DOI	10.11648/j.acis.20140205.11
Page(s)	71-80
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2014. Published by Science Publishing Group

Keywords

Artificial Bee Colony, Numerical Optimization, CEC 2014 Competition, Overall Degradation Strategy, Evolutionary Algorithm

References

[1]	B. Li, R. Chiong and R. Zhang, Balancing Exploration and Exploitation: An Analysis of the Balance-Evolution Artificial Bee Colony Algorithm, unpublished.
[2]	D. Dasgupta and Z. Michalewicz (Eds.). Evolutionary algorithms in engineering applications. Springer Berlin Heidelberg, 1997.
[3]	D. Karaboga and B. Akay, A modified artificial bee colony (ABC) algorithm for constrained optimization problems, Applied Soft Computing, Vol. 11, No. 3, pp. 3021-3031, 2011.
[4]	D. Karaboga and B. Basturk, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm, Journal of global optimization, Vol. 39, No. 3, pp. 459-471, 2007.
[5]	B. Li, L. G. Gong and C. H. Zhao, Unmanned combat aerial vehicles path planning using a novel probability density model based on Artificial Bee Colony algorithm, In 2013 Fourth International Conference on Intelligent Control and Information Processing (ICICIP 2013), pp. 620-625, IEEE, 2013.
[6]	H. Duan, S. Shao, B. Su and L. Zhang, New development thoughts on the bio-inspired intelligence based control for unmanned combat aerial vehicle, Science China Technological Sciences, Vol. 53, No. 8, pp. 2025-2031, 2010.
[7]	B. Li, L. G. Gong and W. L. Yang, An improved Artificial Bee Colony algorithm based on balance-evolution strategy for unmanned combat aerial vehicle path planning, The Scientific World Journal, Vol. 2014, No. 23704, pp. 1-10, 2014.
[8]	B. Li, L. G. Gong and Y. Yao, On the performance of internal feedback artificial bee colony algorithm (IF-ABC) for protein secondary structure prediction. In 2013 Sixth International Conference on Advanced Computational Intelligence (ICACI 2013), pp. 33-38, IEEE, 2013.
[9]	H. Sun, H. Luş and R. Betti, Identification of structural models using a modified Artificial Bee Colony algorithm, Computers & Structures, Vol. 116, pp. 59-74, 2013.
[10]	B. Li, Y. Li and L. G. Gong, Protein secondary structure optimization using an improved artificial bee colony algorithm based on AB off-lattice model, Engineering Applications of Artificial Intelligence, Vol. 27, pp. 70-79, 2014.
[11]	R. J. Kuo, Y. D. Huang, C. C. Lin, Y. H. Wu and F. E. Zulvia, Automatic kernel clustering with bee colony optimization algorithm, Information Sciences, Vol. 283, pp. 107-122, 2014.
[12]	B. Li, Research on WNN modeling for gold price forecasting based on improved Artificial Bee Colony algorithm, Computational intelligence and neuroscience, Vol. 2014, No. 270658, pp. 1-10, 2014.
[13]	Q. K. Pan, M. Tasgetiren, P. N. Suganthan and T. J. Chua, A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem, Information sciences, Vol. 181, No. 12, pp. 2455-2468, 2011.
[14]	J. Q. Li, Q. K.Pan and K. Z. Gao, Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems, The International Journal of Advanced Manufacturing Technology, Vol. 55, pp. 1159-1169, 2011.
[15]	L. Wang, G. Zhou, Y. Xu, S. Wang and M. Liu, An effective artificial bee colony algorithm for the flexible job-shop scheduling problem, The International Journal of Advanced Manufacturing Technology, Vol. 60, No. 4, pp. 303-315, 2012.
[16]	M. F. Tasgetiren, Q. K. Pan, P. N. Suganthan and A. H. Chen, A discrete artificial bee colony algorithm for the total flowtime minimization in permutation flow shops, Information Sciences, Vol. 181, No. 16, pp. 3459-3475, 2011.
[17]	B. Li, L. G. Gong and Y. Li, A Novel Artificial Bee Colony Algorithm Based on Internal-Feedback Strategy for Image Template Matching, The Scientific World Journal, Vol. 2014, No. 906861, pp. 1-14, 2014.
[18]	C. Chidambaram and H. S. Lopes, An improved artificial bee colony algorithm for the object recognition problem in complex digital images using template matching, International Journal of Natural Computing Research, Vol. 1, No. 2, pp. 54-70, 2010.
[19]	B. Li and Y. Yao, An edge-based optimization method for shape recognition using atomic potential function, Engineering Applications of Artificial Intelligence, Vol. 35, pp. 14-25, 2014.
[20]	C. Xu and H. Duan, Artificial bee colony (ABC) optimized edge potential function (EPF) approach to target recognition for low-altitude aircraft, Pattern Recognition Letters, Vol. 31, No. 13, pp. 1759-1772, 2010.
[21]	W. F. Gao, S. Y. Liu and L. L. Huang, A novel artificial bee colony algorithm with Powell's method, Applied Soft Computing, Vol. 13, No. 9, pp. 3763-3775, 2013.
[22]	F. Kang, J. Li and Z. Ma, Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions, Information Sciences, Vol. 181, No. 16, pp. 3508-3531, 2011.
[23]	G. Zhu and S. Kwong, Gbest-guided artificial bee colony algorithm for numerical function optimization, Applied Mathematics and Computation, Vol. 217, No. 7, pp. 3166-3173, 2010.
[24]	G. Q. Li, P. Niu and X. Xiao, Development and investigation of efficient artificial bee colony algorithm for numerical function optimization, Applied soft computing, Vol. 12. No. 1, pp. 320-332, 2012.
[25]	W. L. Xiang and M. Q. An, An efficient and robust artificial bee colony algorithm for numerical optimization, Computers & Operations Research, Vol. 40, No. 5, pp. 1256-1265, 2013.
[26]	A. Alizadegan, B. Asady and M. Ahmadpour, Two modified versions of artificial bee colony algorithm, Applied Mathematics and Computation, Vol. 225, pp. 601-609, 2013.
[27]	B. Li and Y. Li, Y, BE-ABC: hybrid artificial bee colony algorithm with balancing evolution strategy, In 2012 Third International Conference on Intelligent Control and Information Processing (ICICIP 2012), pp. 217-222, IEEE, 2012.
[28]	B. Li, R. Chiong and L. G. Gong, Search-Evasion Path Planning for Submarines Using the Artificial Bee Colony Algorithm, In Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2014), pp. 528-625, IEEE, 2014.
[29]	D. Karaboga and B. Basturk, B, On the performance of artificial bee colony (ABC) algorithm, Applied soft computing, Vol. 8, No. 1, pp. 687-697, 2008.
[30]	J. J. Liang, B. Y. Qu and P. N. Suganthan, Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization, Technical Report 201311, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore, 2013..
[31]	B. Li and Y. Li, A novel image matching method via lateral inhibition using balance-evolution artificial bee colony (BE-ABC) algorithm, submitted.

Cite This Article

Plain Text BibTeX RIS

APA Style

Bai Li. (2014). A Novel Artificial Bee Colony Algorithm with an Overall-Degradation Strategy and Its Performance on the Benchmark Functions of CEC 2014 Special Session. Automation, Control and Intelligent Systems, 2(5), 71-80. https://doi.org/10.11648/j.acis.20140205.11

Copy | Download

ACS Style

Bai Li. A Novel Artificial Bee Colony Algorithm with an Overall-Degradation Strategy and Its Performance on the Benchmark Functions of CEC 2014 Special Session. Autom. Control Intell. Syst. 2014, 2(5), 71-80. doi: 10.11648/j.acis.20140205.11

Copy | Download

AMA Style

Bai Li. A Novel Artificial Bee Colony Algorithm with an Overall-Degradation Strategy and Its Performance on the Benchmark Functions of CEC 2014 Special Session. Autom Control Intell Syst. 2014;2(5):71-80. doi: 10.11648/j.acis.20140205.11

Copy | Download

@article{10.11648/j.acis.20140205.11,
  author = {Bai Li},
  title = {A Novel Artificial Bee Colony Algorithm with an Overall-Degradation Strategy and Its Performance on the Benchmark Functions of CEC 2014 Special Session},
  journal = {Automation, Control and Intelligent Systems},
  volume = {2},
  number = {5},
  pages = {71-80},
  doi = {10.11648/j.acis.20140205.11},
  url = {https://doi.org/10.11648/j.acis.20140205.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20140205.11},
  abstract = {The artificial bee colony (ABC) algorithm has been a well-known swarm intelligence algorithm, which assimilates the cooperating behavior of bees when seeking for nectar sources. Aiming to improve the conventional ABC algorithm, we focus on the re-initialization phase. In this paper, an overall-degradation-oriented artificial bee colony (OD-ABC) algorithm is proposed, pursuing to fight against premature convergence. This is achieved through re-initializing majority of the employed bees at one time, rather than generating at most one scout bee in each iteration. In this work, our OD-ABC algorithm is compared against the conventional ABC algorithms using 24 benchmark functions that origin from the CEC 2014’s competition on single objective real-parameter numerical optimization. The numerical results show that the OD-ABC algorithm is effective and thus can be employed to fight against premature convergence.},
 year = {2014}
}

Copy | Download

TY  - JOUR
T1  - A Novel Artificial Bee Colony Algorithm with an Overall-Degradation Strategy and Its Performance on the Benchmark Functions of CEC 2014 Special Session
AU  - Bai Li
Y1  - 2014/09/30
PY  - 2014
N1  - https://doi.org/10.11648/j.acis.20140205.11
DO  - 10.11648/j.acis.20140205.11
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 71
EP  - 80
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20140205.11
AB  - The artificial bee colony (ABC) algorithm has been a well-known swarm intelligence algorithm, which assimilates the cooperating behavior of bees when seeking for nectar sources. Aiming to improve the conventional ABC algorithm, we focus on the re-initialization phase. In this paper, an overall-degradation-oriented artificial bee colony (OD-ABC) algorithm is proposed, pursuing to fight against premature convergence. This is achieved through re-initializing majority of the employed bees at one time, rather than generating at most one scout bee in each iteration. In this work, our OD-ABC algorithm is compared against the conventional ABC algorithms using 24 benchmark functions that origin from the CEC 2014’s competition on single objective real-parameter numerical optimization. The numerical results show that the OD-ABC algorithm is effective and thus can be employed to fight against premature convergence.
VL  - 2
IS  - 5
ER  -

Copy | Download

Author Information

Bai Li

School of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, China

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Bai Li. (2014). A Novel Artificial Bee Colony Algorithm with an Overall-Degradation Strategy and Its Performance on the Benchmark Functions of CEC 2014 Special Session. Automation, Control and Intelligent Systems, 2(5), 71-80. https://doi.org/10.11648/j.acis.20140205.11

Copy | Download

ACS Style

Bai Li. A Novel Artificial Bee Colony Algorithm with an Overall-Degradation Strategy and Its Performance on the Benchmark Functions of CEC 2014 Special Session. Autom. Control Intell. Syst. 2014, 2(5), 71-80. doi: 10.11648/j.acis.20140205.11

Copy | Download

AMA Style

Bai Li. A Novel Artificial Bee Colony Algorithm with an Overall-Degradation Strategy and Its Performance on the Benchmark Functions of CEC 2014 Special Session. Autom Control Intell Syst. 2014;2(5):71-80. doi: 10.11648/j.acis.20140205.11

Copy | Download

@article{10.11648/j.acis.20140205.11,
  author = {Bai Li},
  title = {A Novel Artificial Bee Colony Algorithm with an Overall-Degradation Strategy and Its Performance on the Benchmark Functions of CEC 2014 Special Session},
  journal = {Automation, Control and Intelligent Systems},
  volume = {2},
  number = {5},
  pages = {71-80},
  doi = {10.11648/j.acis.20140205.11},
  url = {https://doi.org/10.11648/j.acis.20140205.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20140205.11},
  abstract = {The artificial bee colony (ABC) algorithm has been a well-known swarm intelligence algorithm, which assimilates the cooperating behavior of bees when seeking for nectar sources. Aiming to improve the conventional ABC algorithm, we focus on the re-initialization phase. In this paper, an overall-degradation-oriented artificial bee colony (OD-ABC) algorithm is proposed, pursuing to fight against premature convergence. This is achieved through re-initializing majority of the employed bees at one time, rather than generating at most one scout bee in each iteration. In this work, our OD-ABC algorithm is compared against the conventional ABC algorithms using 24 benchmark functions that origin from the CEC 2014’s competition on single objective real-parameter numerical optimization. The numerical results show that the OD-ABC algorithm is effective and thus can be employed to fight against premature convergence.},
 year = {2014}
}

Copy | Download

TY  - JOUR
T1  - A Novel Artificial Bee Colony Algorithm with an Overall-Degradation Strategy and Its Performance on the Benchmark Functions of CEC 2014 Special Session
AU  - Bai Li
Y1  - 2014/09/30
PY  - 2014
N1  - https://doi.org/10.11648/j.acis.20140205.11
DO  - 10.11648/j.acis.20140205.11
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 71
EP  - 80
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20140205.11
AB  - The artificial bee colony (ABC) algorithm has been a well-known swarm intelligence algorithm, which assimilates the cooperating behavior of bees when seeking for nectar sources. Aiming to improve the conventional ABC algorithm, we focus on the re-initialization phase. In this paper, an overall-degradation-oriented artificial bee colony (OD-ABC) algorithm is proposed, pursuing to fight against premature convergence. This is achieved through re-initializing majority of the employed bees at one time, rather than generating at most one scout bee in each iteration. In this work, our OD-ABC algorithm is compared against the conventional ABC algorithms using 24 benchmark functions that origin from the CEC 2014’s competition on single objective real-parameter numerical optimization. The numerical results show that the OD-ABC algorithm is effective and thus can be employed to fight against premature convergence.
VL  - 2
IS  - 5
ER  -

Copy | Download