Opposition-based Artificial Bee Colony algorithm

Genetic and Evolutionary Computation Conference, GECCO'11

Volumn , Issue , 2011, Pages 109-115

  El Abd, Mohammed ^a  

^a AMERICAN UNIVERSITY OF KUWAIT   (Kuwait)

Author keywords

Benchmarking; Black box optimization; Opposition based learning; Swarm intelligence

Indexed keywords

ARTIFICIAL BEE COLONIES; BLACK-BOX OPTIMIZATION; DIFFERENTIAL EVOLUTION; FORAGING BEHAVIORS; FUNCTION OPTIMIZATION; HONEY BEE; INITIALIZATION STEP; OPPOSITION-BASED LEARNING; SWARM INTELLIGENCE;

ARTIFICIAL INTELLIGENCE; BENCHMARKING; BIOLOGY; CELLULAR AUTOMATA; OPTIMIZATION;

ALGORITHMS;

EID: 84860391793     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2001576.2001592     Document Type: Conference Paper

References (21)

1. B. Akay and D. Karaboga. Parameter tuning for the artificial bee colony algorithm. In Proceedings of 1st International Conference on Computational Collective Intelligence, pages 608-619. Springer-Verlag: Berlin Heidelberg, 2009.
2. B. Akay and D. Karaboga. A modified artificial bee colony algorithm for real-parameter optimization. Information Sciences, doi:10.1016/j.ins.2010.07. 015, 2010.
3. B. Basturk and D. Karaboga. An artificial bee colony (ABC) algorithm for numeric function optimization. In Proceedings of the IEEE Swarm Intelligence Symposium, pages 66-66. IEEE Press, 2006.
4. M. El-Abd. Black-box optimization benchmarking for noiseless function testbed using artificial bee colony algorithm. In Accepted as GECCO (Companion) in Black-Box Optimization Benchmarking Workshop, 2010.
5. S. Finck, N. Hansen, R. Ros, and A. Auger. Real-parameter black-box optimization benchmarking 2010: Presentation of the noiseless functions. Technical Report 2009/20, Research Center PPE, 2010.
6. L. Han and X. He. A novel opposition-based particle swarm optimization for noisy problems. In Proceedings of Third International Conference on Natural Computation, ICNC, pages 624-629, 2007.
7. N. Hansen, A. Auger, S. Finck, and R. Ros. Real-parameter black-box optimization benchmarking 2010: Experimental setup. Technical Report RR-7215, INRIA, 2010.
8. N. Hansen, S. Finck, R. Ros, and A. Auger. Real-parameter black-box optimization benchmarking 2010: Noiseless functions definitions. Technical Report RR-6829, INRIA, 2010.
9. D. Karaboga. An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Engineering Faculty, Computer Engineering Department, Erciyes University, 2005.
10. D. Karaboga. Artificial bee colony code. http://mf.erciyes.edu.tr/abc/ software.htm, 2008.
11. D. Karaboga and B. Akay. Artificial bee colony (abc), harmony search and bees algorithms on numerical optimization. In Proceedings of Innovative Produxction Machines and Systems Virtual Conference, IPROMS, 2009.
12. D. Karaboga and B. Akay. A comparative study of artificial bee colony algorithm. Applied Mathematics and Computation, 214:108-132, 2009.
13. D. Karaboga and B. Basturk. A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3):459-471, 2007. (Pubitemid 47551831)
14. D. Karaboga and B. Basturk. On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing, 8(1):687-697, 2008. (Pubitemid 47374599)
15. K. M. Passino. Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems Magazine, 5(3):52-67, 2002. (Pubitemid 34711477)
16. K. Price. Differential evolution vs. the functions of the second ICEO. In Proceedings of the IEEE International Congress on Evolutionary Computation, pages 153-157, 1997.
17. S. Rahnamayan, H. Tizhoosh, and M. M. A. Salama. Opposition-based differential evolution algorithms. In Proceedings of IEEE Congress on Evolutionary Computation, pages 2010-2017, 2006. (Pubitemid 47130743)
18. S. Rahnamayan, H. Tizhoosh, and M. M. A. Salama. Opposition-based differential evolution. IEEE Transactions on Evolutionary Computation, 12(1):64-79, 2008. (Pubitemid 351330332)
19. P. N. Suganthan, N. Hansen, J. J. Liang, K. Deb, Y.-P. Chen, A. Auger, and S. Tiwari. Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. Technical Report 2005005, ITT Kanpur, India, 2005.
20. H. Tizhoosh. Opposition-based learning: A new scheme for machine intelligence. In Proceedings of International Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA, pages 695-701, 2005.
21. H. Wang, Y. Liu, S. Zeng, H. Li, and C. Li. Opposition-based particle swarm algorithm with cauchy mutation. In Proceedings of IEEE Congress on Evolutionary Computation, pages 4750-4756, 2007.