Geodesic
Collective bionic algorithm with biogeography based migration operator for binary optimization
Žurnal Sibirskogo federalʹnogo universiteta. Matematika i fizika, Tome 9 (2016) no. 1, pp. 3-10.

Voir la notice de l'article provenant de la source Math-Net.Ru

The meta-heuristic called Co-Operation of Biology Related Algorithms (COBRA) developed earlier for solving real-valued optimization problems has also been modified for solving optimization problems with binary variables (COBRA-b). The algorithm COBRA-b is based on a collective work of five nature-inspired algorithms' binary modifications such as Particle Swarm Optimization (PSO), the Wolf Pack Search Algorithm (WPS), the Firefly Algorithm (FFA), the Cuckoo Search Algorithm (CSA) and Bat Algorithm (BA). Its usefulness and workability were demonstrated on various benchmarks, and COBRA-b also outperformed its components. But solving problems sometimes required too many function evaluations, so the COBRA-b migration operator was modified by integrating biogeography principles for the speedup of the algorithm. Numerical experiments showed that the new modification exhibits high performance and outperforms COBRA-b and therefore its components.
Keywords: biology inspired algorithms, biogeography, migration operator, optimization, binary variables. 
@article{JSFU_2016_9_1_a0,
     author = {Shakhnaz A. Akhmedova and Eugene S. Semenkin},
     title = {Collective bionic algorithm with biogeography based migration operator for binary optimization},
     journal = {\v{Z}urnal Sibirskogo federalʹnogo universiteta. Matematika i fizika},
     pages = {3--10},
     publisher = {mathdoc},
     volume = {9},
     number = {1},
     year = {2016},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/JSFU_2016_9_1_a0/}
}
TY  - JOUR
AU  - Shakhnaz A. Akhmedova
AU  - Eugene S. Semenkin
TI  - Collective bionic algorithm with biogeography based migration operator for binary optimization
JO  - Žurnal Sibirskogo federalʹnogo universiteta. Matematika i fizika
PY  - 2016
SP  - 3
EP  - 10
VL  - 9
IS  - 1
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/JSFU_2016_9_1_a0/
LA  - en
ID  - JSFU_2016_9_1_a0
ER  - 
%0 Journal Article
%A Shakhnaz A. Akhmedova
%A Eugene S. Semenkin
%T Collective bionic algorithm with biogeography based migration operator for binary optimization
%J Žurnal Sibirskogo federalʹnogo universiteta. Matematika i fizika
%D 2016
%P 3-10
%V 9
%N 1
%I mathdoc
%U http://geodesic.mathdoc.fr/item/JSFU_2016_9_1_a0/
%G en
%F JSFU_2016_9_1_a0
Shakhnaz A. Akhmedova; Eugene S. Semenkin. Collective bionic algorithm with biogeography based migration operator for binary optimization. Žurnal Sibirskogo federalʹnogo universiteta. Matematika i fizika, Tome 9 (2016) no. 1, pp. 3-10. http://geodesic.mathdoc.fr/item/JSFU_2016_9_1_a0/

[1] J. Kennedy, R. Eberhart, “Particle Swarm Optimization”, Proceedings of International Conference on Neural networks IV (1995), 1942–1948

[2] Ch. Yang, X. Tu, J. Chen, “Algorithm of Marriage in Honey Bees Optimization Based on the Wolf Pack Search”, Proceedings of International Conference on Intelligent Pervasive Computing, IPC 2007, 2007, 462–467

[3] X. S. Yang, “Firefly algorithms for multimodal optimization”, Proceedings of 5th Symposium on Stochastic Algorithms, Foundations and Applications, SAGA 2009, 2009, 169–178 | DOI | MR | Zbl

[4] X. S. Yang, S. Deb, “Cuckoo Search via Levy flights”, Proceedings of World Congress on Nature and Biologically Inspired Computing, NaBic 2009, 2009, 210–214 | DOI

[5] X. S. Yang, “A new metaheuristic bat-inspired algorithm”, Nature Inspired Cooperative Strategies for Optimization, Studies in Computational Intelligence, 284, 2010, 65–74 | DOI | Zbl

[6] Sh. Akhmedova, E. Semenkin, “Co-Operation of Biology Related Algorithms”, Proceedings of the IEEE Congress on Evolutionary Computation, CEC'2013, 2013, 2207–2214

[7] Sh. Akhmedova, E. Semenkin, “Co-Operation of Biology Related Algorithms Meta-Heuristic in ANN-Based Classifiers Design”, Proceedings of the IEEE World Congress on Computational Intelligence, WCCI'2014, 2014, 867–873

[8] Sh. Akhmedova, E. Semenkin, “New optimization metaheuristic based on co-operation of biology related algorithms”, Vestnik. Bulletine of Siberian State Aerospace University, 50:4 (2013), 92–99

[9] D. Simon, “Biogeography-Based Optimization”, IEEE Transactions on Evolutionary Computation, 12:6 (2008), 702–713 | DOI

[10] R. H. MacArthur, E. O. Wilson, “An Equilibrium Theory of Insular Zoogeography”, Journal of Evolution, 17:4 (1963), 373–387 | DOI

[11] J. J. Liang, B. Y. Qu, P. N. Suganthan, A. G. Hernandez-Diaz, Problem Definitions and Evaluation Criteria for the CEC 2013 Special Session on Real-Parameter Optimization, Technical Report, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China, 2012; Technical Report, Nanyang Technological University, Singapore

[12] J. Kennedy, R. C. Eberhart, “A discrete binary version of the particle swarm algorithm”, Proceedings of the World Multiconference on Systemics, Cybernetics and Informatics (1997), 4104–4109

[13] D. Simon, “A Probabilistic Analysis of a Simplified Biogeography-Based Optimization Algorithm”, Evolutionary Computation, 2011, 167–188 | DOI

[14] A. Alroomi, F. Albasri, J. Talaq, “Performance comparison between the original forms of biogeography-based optimization algorithms”, Proceedings of the Second International Conference on Advanced Information Technologies and Applications, 2013, 121–140 | DOI

[15] M. Molga, Cz. Smutnicki, Test functions for optimization need, 3 kwietnia, 2005