Blend of local and global variant of PSO in ABC

2013 World Congress on Nature and Biologically Inspired Computing, NaBIC 2013

Volumn , Issue , 2013, Pages 113-119

  Sharma, Tarun Kumar ^a   Pant, Millie ^b   Abraham, Ajith ^c,d  

^a THAPAR UNIVERSITY   (India)

^b INDIAN INSTITUTE OF TECHNOLOGY ROORKEE   (India)

^c SCIENTIFIC NETWORK FOR INNOVATION AND RESEARCH EXCELLENCE   (United States)

^d VSB TECHNICAL UNIVERSITY OF OSTRAVA   (Czech Republic)

Author keywords

Artificial bee colony; Metaheuristic; Optimization; PSO; Swarm intelligence

Indexed keywords

ARTIFICIAL BEE COLONIES; ARTIFICIAL BEE COLONIES (ABC); EXPLORATION AND EXPLOITATION; HIGH-QUALITY SOLUTIONS; META-HEURISTIC OPTIMIZATION TECHNIQUES; METAHEURISTIC; PSO; SWARM INTELLIGENCE;

ARTIFICIAL INTELLIGENCE; BLENDING; COMMUNICATION CHANNELS (INFORMATION THEORY); OPTIMIZATION;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 84887847614     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/NaBIC.2013.6617848     Document Type: Conference Paper

References (29)

1. D. Karaboga, An idea based on honey bee swarm for numerical optimization. Technical Report-TR06, Erciyes University; 2005.
2. Karaboga D, Basturk B, Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems, Advances in Soft Computing: Foundations of Fuzzy Logic and Soft Computing, Vol. 4529/2007, No. 0302-9743, 2007, pp.789-798.
3. N. Gomez, L. F. Mingo, J. Bobadilla, F. Serradilla, J. A C. Manzano: Particle Swarm Optimization models applied to Neural Networks using the R language, WSEAS Transactions on Systems, 9(2), 2010, pp. 192-202
4. R. M. Idris, A Khairuddin and M.W. Mustafa, Optimal Allocation of FACTS Devices in Deregulated Electricity Market Using Bees Algorithm, WSEAS Transactions on Power Systems, Vol. 5, Issue 2, Apr 2010, pp. 108-119.
5. AV. Baterina, C. Oppus, Image Edge Detection Using Ant Colony Optimization, WSEAS Transactions On Signal Processing, Vol. 6, No. 2, 2010, pp.58-67.
6. Milan Tuba: Swarm Intelligence Algorithms Parameter Tuning, Proceedings of the American Conference on Applied Mathematics, Harvard, Cambridge, USA, pp. 389-394, 2012.
7. L. Jiann-Horng, H. Li-Ren: Chaotic bee swarm optimization algorithm for path planning of mobile robots, Proceedings of the 10th WSEAS international conference on evolutionary computing, pp. 84-89, 2009.
8. Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings Sixth Symposium on Micro Machine and Human Science, Piscataway, NJ, IEEE Service Center (1995) 39-43G.
9. S.-C. Chu, P.-W. Tsai, and J.-S. Pan, Cat Swarm Optimization, Proc. of 9th Pacific Rim International Conference on Artificial Intelligence, Guilin, China, LNCS 4099, pp.854-858, 2006.
10. M. Dorigo and L. M. Gambardella, Ant colony system: a cooperative learning approach to the traveling salesman problem, IEEE Trans. on Evolutionary Computation, vol.26, no. I, pp.53-66, 1997.
11. D. Karaboga and B. Basturk, On the performance of artitlcial bee colony (ABC) algorithm, Applied Soft Computing, 8 (2008), pp.687-697, 2008.
12. D. Karaboga and B. Basturk, Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems, LNC S: Advances in Soft Computing: Foundations of Fuzzy Logic and Soft Computing, Springer-Verlag, IFSA (2007), pp. 789-798.
13. K. Deb, "An efficient constraint handling method for genetic algorithms," Computer Methods in Applied Mechanics and Engineering, vol. 186, no. 2/4, pp. 311-338, 2000.
14. Zhu, G, Kwong, S., 2010, Gbest-guided artificial bee colony algorithm for numerical function optimization, Applied Mathematics and Computation, 217(7), 3166-3173.
15. Parsopoulos, K. E., Vrahatis, M.N.: UPSO: A unitled particle swarm optimization scheme. In: Lecture Series on Computer and Computational Sciences, Vol. I, Proc. Int. Conf. Computational Methods in Sciences and Engineering, VSP International Science Publishers, Zeist, The Netherlands (2004) 868-873.
16. Runarsson TP, Yao X (2000) Stochastic ranking for constrained evolutionary optimization. IEEE Trans Evol Comput 4(3):284-294.
17. Golberg, D. E., Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Massachusetts, 1989.
18. A Kennedy, J. and R. Eberhart, "Particle swarm optimization," Proceedings of the IEEE International Conference Neural Networks, Vol. 4, 1942-1948, 1995.
19. D. Storn, R. and Price, K., "Differential Evolution - a Simple and Effecient Heuristic for Global Optimization over Continuous Spaces", Journal of Global Optimization, 11 (1997) pp. 341-359.
20. Adjiman, C. S., Androulakis, J.P., Floudas, C.A., A Global Optimization Method, alphaBB, for General Twice-Differentiable Constrained NLPs: II - Implementation and Computational Results, Computers and Chemical Engineering, 1998, Vol:22, 1159-1179.
21. B. Babu, R. Angira, Modified differential evolution (MDE) for optimization of non-linear chemical processes. Computers and Chemical Engineering 30 (2006) 989-1002.
22. S. Kheawhom, Efficient constraint handling scheme for differential evolutionary algorithm in solving chemical engineering optimization problem, Journal of Industrial and Engineering Chemistry, 16 (2010) 620-628.
23. Floudas, C. A, & Pardalos, P. M. (1990). A collection of test problems for constrained global optimization algorithms. Lecture notes in computer science: vol. 455. Berlin, Germany: Springer.
24. H. Izakian, A Abraham, V. Snasel, Comparison of Heuristics for Scheduling Independent Tasks on Heterogeneous Distributed Environments, The 2009 IEEE International Workshop on HPC and Grid Applications (IWHGA2009), IEEE Press, pp. 8-12, 2009.
25. S. Das, A Biswas, S. Dasgupta and A Abraham, Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications, Foundations of Computational Intelligence Volume 3: Global Optimization, Studies in Computational Intelligence, Springer Verlag, Germany, ISBN: 978-3-642-01084-2, pp. 23-55, 2009.
26. Hongbo Liu, Ajith Abraham and Maurice Clerc, Chaotic Dynamic Characteristics in Swarm Intelligence, Applied Soft Computing Journal, Elsevier Science, Volume 7, Issue 3, pp. 1019-1026, 2007.
27. F. Xhafa and A Abraham, Meta-heuristics for Grid Scheduling Problems, Metaheuristics for Scheduling: Distributed Computing Environments, Studies in Computational Intelligence, Springer Verlag, Germany, ISBN: 978-3-540-69260-7, pp. 1-37, 2008.
28. R. Thangaraj, M. Pant, A Abraham and P. Bouvry, Particle Swarm Optimization: Hybridization Perspectives and Experimental lllustrations, Applied Maths and Computation, Elsevier Science, Netherlands, Volume 217, No. I, pp. 5208-5226, 2011.
29. H. Liu, A Abraham, O.K. Choi and S.H. Moon, Variable Neighborhood Particle Swarm Optimization for Multi-objective Flexible Job-shop Scheduling Problems, The Sixth International Conference on Simulated Evolution And Learning (SEAL06), China, Springer Verlag, Germany, Lecture Notes in Computer Science, T.D. Wang et al. (Eds.): SEAL 2006, LNCS 4247, pp. 197-204, 2006.