Email this article 
A Hybrid Swarm Intelligence Optimization for Benchmark Models by Blending PSO with ABC
(*) Corresponding author
DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)
Abstract
In swarm intelligence PSO is not so popular algorithm but ABC is recently developed and most popular whereas PSO is lagging in finding global solutions however the ABC’s neighborhood search is not sufficient to accelerate the convergence rate. The hybrid technique is developed in such a way that it can solve the issues arising in individual PSO and ABC. As ABC outperforms in the most problems, it will be selected as the primary algorithm and the swarming behavior of the particles are included in the bees. A compromising neighborhood search model is developed for ABC to aid accelerated neighborhood search by considering the property of PSO’s particles updating behavior along with the ABC’s neighborhood search. The introduction of such neighborhood search model fine tunes the neighborhood search property of employed and onlooker bees that helps to converge faster than conventional ABC and PSO. The tests will be carried out using standard benchmark test function models and the performance will be compared against the individual PSO and ABC algorithms.
Copyright © 2013 Praise Worthy Prize - All rights reserved.
Keywords
References
Milos Subotic, “Artificial bee colony algorithm with multiple onlookers for constrained optimization problems”, Proceedings of the 5th European conference on European computing conference, pp. 251-256, 2011.
David Martens, Bart Baesens, Tom Fawcett “Editorial survey: swarm intelligence for data mining”, Journal Machine Learning archive, Vol. 82, No. 1, pp. 1-42, January 2011.
Daniel W. Palmer, Marc Kirschenbaum, Michael A. Kovacina, Jon P. Murton, Kelly M. Zajac, “Self-referential Biological Inspiration: Humans Observing Human Swarms to Identify Swarm Programming Techniques”, 7th World Multi-Conference on Systemic, Cybernetics & Informatics (SCI) Orlando, FL, pp. 1-6, 2003.
T. Hashni, T. Amudha, “Relative Study of CGS with ACO and BCO Swarm Intelligence Techniques”, International Journal of Computer Technology &Applications,Vol 3, No. 5, pp. 1775-1781, 2012.
AmitGarg, Pawan Gill, ParveenRathi, Amardeep and K. K Garg, “An Insight into Swarm Intelligence”, International Journal of Recent Trends in Engineering, Vol 2, No. 8, pp. 42-44, November 2009.
Manish Mahant, Bharat Choudhary, AbhishekKesharwani, Kalyani Singh Rathore, “A Profound Survey on Swarm Intelligence”, International Journal of Advanced Computer Research, Vol. 2, No. 1, pp. 31-36, March 2012.
Binitha S, S Siva Sathya, “A Survey of Bio inspired Optimization Algorithms”, International Journal of Soft Computing and Engineering, Vol. 2, No. 2, pp. 137-151, May 2012.
Leandro Nunes de Castro, “Fundamentals of natural computing: an overview”, Physics of Life Reviews, Vol. 4, No. 1, pp. 1–36, March 2007.
Jorge A. Ruiz-Vanoye, OcotlánDíaz-Parra, Felipe Cocón, Andrés Soto, “Meta-Heuristics Algorithms based on the Grouping of Animals by Social Behavior for the Traveling Salesman Problem”, International Journal of Combinatorial Optimization Problems and Informatics, Vol. 3, No. 3, Sep.-Dec. 2012.
Shivakumar B L, Amudha T, “A Novel Nature-inspired Algorithm to solve Complex Generalized Assignment Problems”, International Journal of Research and Innovation in Computer Engineering , Vol 2, No. 3, pp. 280-284, June 2012.
Yamille del Valle, Ganesh Kumar Vinayagamoorthy, Salman Mohagheghi, Jean-Carlos Hernandez, and Ronald G. Harley, “Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems”, IEEE Transactions on Evolutionary Computation, Vol. 12, No. 2, pp. 171-195, April 2008.
R. Umarani, V. Selvi,“Particle Swarm Optimization-Evolution, Overview and Applications”, International Journal of Engineering Science and Technology, Vol. 2, No.7, pp. 2802-2806, 2010.
SujathaBalaraman, N. kamaraj, “Application of Differential Evolution for Congestion Management in Power System”, Modern Applied Science, Vol. 4, No. 8, pp. 33-42, August 2010.
Rhythm S. Wadhwa, Terje Lien, “Electromagnet Shape Optimization using Improved Discrete Particle Swarm Optimization”, Excerpt from the Proceedings of COMSOL Conference in Stuttgart, pp. 1-6, 2011
Chettih, S., Khiat, M., Chaker, A., Means optimization of reactive power compensation using the particle swarm optimization PSO method: Application in the western Algerian transmission system, (2009) International Review of Electrical Engineering (IREE), 4 (4), pp. 622-626.
NadezdaStanarevic, Milan Tuba, and NebojsaBacanin, “Modified artificial bee colony algorithm for constrained problems optimization”, International Journal of Mathematical Models and Methods In Applied Sciences, Vol, 5, No. 3, pp. 641-655, 2011.
Mustafa ServetKiranandMesutGunduz, "A Novel Artificial Bee colony based algorithm for solving the number optimization problem", International Journal of Innovative Computing, Information and Control, Vol. 8, No. 9, Sep. 2012.
Arun Kumar, Rajeshwar Singh, “Mobile Ad Hoc Networks Routing Optimization Techniques Using Swarm Intelligence”, International Journal of Research in IT & Management, Vol. 1, No. 4, August, 2011.
Milos Subotic, Milan Tuba and NadezdaStanarevic, “Different approaches in parallelization of the artificial bee colony algorithm”, International Journal of Mathematical Models And Methods in Applied Sciences, Vol. 5, No. 4, pp. 755-762, 2011.
O.A. Mohamed Jafar and R. Sivakumar, “Ant-based Clustering Algorithms: A Brief Survey”, International Journal of Computer Theory and Engineering, Vol. 2, No. 5, 1793-8201, October, 2010
S.M. ELseuofi, “Quality Of Service Using Pso Algorithm”, International Journal of Computer Science & Information Technology, Vol 4, No 1, pp. 165-175, Feb 2012.
V. Selvi, R.Umarani, “Comparative Study of Swarm Intelligence Techniques”, International Journal of Research in Engineering Design, Vol 01, No. 01, pp. 37-41, April - July 2012.
PinfaBoonrong, BoonsermKaewkamnerdpong, “Canonical PSO based Nanorobot Control for Blood Vessel Repair”, World Academy of Science, Engineering and Technology, Vol. 58, 2011.
BhartiSuri, Snehlata, “Review of Artificial Bee Colony Algorithm to Software Testing”, International Journal of Research and Reviews in Computer Science, Vol. 2, No. 3, pp. 706-711, June 2011.
DervisKaraboga, BahriyeAkay, “A comparative study of Artificial Bee Colony algorithm”, Applied Mathematics and Computation, Vol. 214, No. 1, pp. 108-132, 1 August 2009.
Aloysius George, B. R. Rajakumar, D. Binu “Genetic algorithm based airlines booking terminal open/close decision system”, Proceedings of the International Conference on Advances in Computing, Communications and Informatics, pages 174-179, 2012
Aloysius George and B. R. Rajakumar, “Fuzzy Aided Ant Colony Optimization Algorithm to Solve Optimization Problem” Advances in Intelligent Systems and Computing, Intelligent Informatics Volume 182, pp 207-215, 2013
Khorshidi, Reza, An Efficient Hybrid Evolutionary Optimization Algorithm for Multi-Objective Distribution Feeder Reconfiguration,(2009) International Review of Electrical Engineering (IREE), 4 (6), pp. 1318-1325.
Slimani, L., Bouktir, T., Optimal power flow using artificial bee colony with incorporation of FACTS devices: A case study, (2011) International Review of Electrical Engineering (IREE), 6 (7), pp. 3091-3101.
Refbacks
- There are currently no refbacks.
Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize
