Using Cooperation Approaches at Different Levels of Artificial Bee Colony Method
In this work, a Multi-Level Artificial Bee Colony
(called MLABC) for optimizing numerical test functions is presented.
In MLABC, two species are used. The first species employs n
colonies where each of them optimizes the complete solution vector.
The cooperation between these colonies is carried out by exchanging
information through a leader colony, which contains a set of elite
bees. The second species uses a cooperative approach in which the
complete solution vector is divided to k sub-vectors, and each of
these sub-vectors is optimized by a colony. The cooperation between
these colonies is carried out by compiling sub-vectors into the
complete solution vector. Finally, the cooperation between two
species is obtained by exchanging information. The proposed
algorithm is tested on a set of well-known test functions. The results
show that MLABC algorithm provides efficiency and robustness to
solve numerical functions.
[1] D. Karaboga, B. Basturk, “A powerful and efficient algorithm for
numerical function optimization: artificial bee colony (ABC) algorithm”,
in Journal of Global Optimization vol. 39, pp. 459–471, 2007.
[2] R. Akbari, A. Mohammadi, K. Ziarati, A powerful bee swarm
optimization algorithm, in: Proceedings of the IEEE 13th Multitopic
Conference, 2009, pp. 1–6.
[3] D.T. Pham, A. Ghanbarzadeh, E. Koc, S. Otri, S. Rahim, M. Zaidi, The
bees algorithm—a novel tool for complex optimisation problems,
Proceedings of the Intelligent Production Machines and Systems (2006)
454–459.
[4] M. A. Potter and K. A. de Jong, “A cooperative coevolutionary approach
to function optimization,” in the Third Parallel Problem Solving from
Nature. Berlin, Germany: Springer-Verlag, 1994, pp. 249–257.
[5] F. van den Bergh, and A. P. Engelbrecht, “Cooperative learning in
neural networks using particle swarm optimizers,” South African
Comput. J., vol. 26, pp. 84–90, 2000.
[6] F. van den Bergh, and A. P. Engelbrecht, “A Cooperative Approach to
Particle Swarm Optimization”, in IEEE Transactions on Evolutionary
Computation, Vol. 8, No. 3, pp. 225-239, 2004.
[7] W. Zou, Y. Zhu, H. Chen, and Z. Zhu, Cooperative Approaches to
Artificial Bee Colony Algorithm, 2010 International Conference on
Computer Application and System Modeling (ICCASM 2010), Vol. 9,
pp. 44-48 2010.
[8] R. Akbari, V. Zeighami, K. Ziarati, A Cooperative Artificial Bee Colony
Optimizer, 7th International Industrial Engineering Conference, Isfahan,
Iran, pp. 1-10, 2010.
[9] M. El-Abd, A Cooperative Approach to The Artificial Bee Colony
Algorithm, IEEE World Congress on Evolutionary Computation, 2010.
[10] S. Baskar and P. N. Suganthan, “A novel concurrent particle swarm
optimization,” in Proc. of IEEE Congress on Evolutionary Computation,
vol.1, pp. 792– 796, 2004.
[11] M. Belal and T. El-Ghazawi, “Parallel models for particle swarm
optimizers,” in International Journal on Intelligent Cooperative
Information Systems, vol. 4, no. 1, pp. 100–111, 2004.
[12] Ben Niu, Y. Zhu, X. He, H. Wu, “MCPSO: A multi-swarm cooperative
particle swarm optimizer”, In Journal of Applied Mathematics and
Computation, 185, pp. 1050–1062, 2007.
[13] M. El-Abd, and M. S. Kamel, ” A Taxonomy of Cooperative Particle
Swarm Optimizers”, in International Journal of Computational
Intelligence Research, Vol.4, No.2, pp.137–144, 2008.
[14] R. Akbari, and K. Ziarati, A Cooperative Approach to Bee Swarm
Optimization, Journal of Information Science and Engineering, Vol. 27,
No. 3, pp. 799-818, 2011.
[15] V. Zeighami, R. Akbari, and K. Ziarati, "An Efficient Multi Population
Artificial Bee Colony," International Journal of Machine Learning and
Computing vol. 2, no. 3, pp. 195-199, 2012.
[16] R. Akbari, V. Zeighami, and K. Ziarati, “MLGA: A Multilevel
Cooperative Genetic Algorithm”, IEEE Fifth International Conference
on Bio-Inspired Computing: Theories and Applications (BIC-TA 2010),
pp. 271-277, September 2010.
[17] E. Sober, and D. S. Wilson, “Unto others, the evolution and psychology
of unselfish behavior”, Harvard University Press, 1998.
[18] R. Bergmuller, A. F. Russell, R. A. Johnstone, R. Bshary, “On the
further integration of cooperative breeding and cooperation theory”, in
Journal of Behavioural Processes, 76 (2007), pp. 170–181.
[1] D. Karaboga, B. Basturk, “A powerful and efficient algorithm for
numerical function optimization: artificial bee colony (ABC) algorithm”,
in Journal of Global Optimization vol. 39, pp. 459–471, 2007.
[2] R. Akbari, A. Mohammadi, K. Ziarati, A powerful bee swarm
optimization algorithm, in: Proceedings of the IEEE 13th Multitopic
Conference, 2009, pp. 1–6.
[3] D.T. Pham, A. Ghanbarzadeh, E. Koc, S. Otri, S. Rahim, M. Zaidi, The
bees algorithm—a novel tool for complex optimisation problems,
Proceedings of the Intelligent Production Machines and Systems (2006)
454–459.
[4] M. A. Potter and K. A. de Jong, “A cooperative coevolutionary approach
to function optimization,” in the Third Parallel Problem Solving from
Nature. Berlin, Germany: Springer-Verlag, 1994, pp. 249–257.
[5] F. van den Bergh, and A. P. Engelbrecht, “Cooperative learning in
neural networks using particle swarm optimizers,” South African
Comput. J., vol. 26, pp. 84–90, 2000.
[6] F. van den Bergh, and A. P. Engelbrecht, “A Cooperative Approach to
Particle Swarm Optimization”, in IEEE Transactions on Evolutionary
Computation, Vol. 8, No. 3, pp. 225-239, 2004.
[7] W. Zou, Y. Zhu, H. Chen, and Z. Zhu, Cooperative Approaches to
Artificial Bee Colony Algorithm, 2010 International Conference on
Computer Application and System Modeling (ICCASM 2010), Vol. 9,
pp. 44-48 2010.
[8] R. Akbari, V. Zeighami, K. Ziarati, A Cooperative Artificial Bee Colony
Optimizer, 7th International Industrial Engineering Conference, Isfahan,
Iran, pp. 1-10, 2010.
[9] M. El-Abd, A Cooperative Approach to The Artificial Bee Colony
Algorithm, IEEE World Congress on Evolutionary Computation, 2010.
[10] S. Baskar and P. N. Suganthan, “A novel concurrent particle swarm
optimization,” in Proc. of IEEE Congress on Evolutionary Computation,
vol.1, pp. 792– 796, 2004.
[11] M. Belal and T. El-Ghazawi, “Parallel models for particle swarm
optimizers,” in International Journal on Intelligent Cooperative
Information Systems, vol. 4, no. 1, pp. 100–111, 2004.
[12] Ben Niu, Y. Zhu, X. He, H. Wu, “MCPSO: A multi-swarm cooperative
particle swarm optimizer”, In Journal of Applied Mathematics and
Computation, 185, pp. 1050–1062, 2007.
[13] M. El-Abd, and M. S. Kamel, ” A Taxonomy of Cooperative Particle
Swarm Optimizers”, in International Journal of Computational
Intelligence Research, Vol.4, No.2, pp.137–144, 2008.
[14] R. Akbari, and K. Ziarati, A Cooperative Approach to Bee Swarm
Optimization, Journal of Information Science and Engineering, Vol. 27,
No. 3, pp. 799-818, 2011.
[15] V. Zeighami, R. Akbari, and K. Ziarati, "An Efficient Multi Population
Artificial Bee Colony," International Journal of Machine Learning and
Computing vol. 2, no. 3, pp. 195-199, 2012.
[16] R. Akbari, V. Zeighami, and K. Ziarati, “MLGA: A Multilevel
Cooperative Genetic Algorithm”, IEEE Fifth International Conference
on Bio-Inspired Computing: Theories and Applications (BIC-TA 2010),
pp. 271-277, September 2010.
[17] E. Sober, and D. S. Wilson, “Unto others, the evolution and psychology
of unselfish behavior”, Harvard University Press, 1998.
[18] R. Bergmuller, A. F. Russell, R. A. Johnstone, R. Bshary, “On the
further integration of cooperative breeding and cooperation theory”, in
Journal of Behavioural Processes, 76 (2007), pp. 170–181.
@article{"International Journal of Engineering, Mathematical and Physical Sciences:68581", author = "Vahid Zeighami and Mohsen Ghasemi and Reza Akbari", title = "Using Cooperation Approaches at Different Levels of Artificial Bee Colony Method", abstract = "In this work, a Multi-Level Artificial Bee Colony
(called MLABC) for optimizing numerical test functions is presented.
In MLABC, two species are used. The first species employs n
colonies where each of them optimizes the complete solution vector.
The cooperation between these colonies is carried out by exchanging
information through a leader colony, which contains a set of elite
bees. The second species uses a cooperative approach in which the
complete solution vector is divided to k sub-vectors, and each of
these sub-vectors is optimized by a colony. The cooperation between
these colonies is carried out by compiling sub-vectors into the
complete solution vector. Finally, the cooperation between two
species is obtained by exchanging information. The proposed
algorithm is tested on a set of well-known test functions. The results
show that MLABC algorithm provides efficiency and robustness to
solve numerical functions.
", keywords = "Artificial bee colony, cooperative artificial bee
colony, multilevel cooperation.", volume = "8", number = "11", pages = "1408-7", }
