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Comparison of Three Evolutionary Algorithms: PSOA, ACOA and BCOA on Recognition Arabic Characters Problem

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DOI: 10.23977/jaip.2017.21001 | Downloads: 33 | Views: 4014


Ahmed Naser Ismael 1, Majida Ali Abed 1


1 College of Computers Sciences & Mathematics, Tikrit University , Tikrit, Iraq

Corresponding Author

Majida Ali Abed


Intelligence techniques such as Particle swarm optimization, Genetic algorithm, Ant colony optimization , Bee Colony Optimization can apply on the system as classification method for better result This paper we survey three techniques Ant Colony Optimization Algorithm (ACOA), Particle Swarm Optimization Algorithm (PSOA), and Bee Colony Optimization Algorithm (BCOA),their algorithm and reason to use. In recent years, the area of Evolutionary Computation has come into these three. Three of the popular developed approaches are Particle Swarm Optimization Algorithm (PSOA), Ant Colony Optimization Algorithm (ACOA) and Bee Colony Optimization Algorithm (BCOA), are used in optimization problems. Since the three approaches are supposed to find a solution to a given objective function but employ different strategies and computational effort, it is appropriate to compare their implementation. The problem area chosen is that recognition of Final forms of Arabic handwritten characters.


Particle swarm optimization Algorithm (PSOA), Ant Colony Optimization Algorithm (ACOA), Bee Colony Optimization Algorithm (BCOA), Recognition system.


Majida Ali Abed, Ahmed Naser Ismael. Comparison of Three Evolutionary Algorithms: PSOA, ACOA and BCOA on Recognition Arabic Characters Problem (2017) Vol. 2, Num. 1: 1-12.


[1] Rahul Kala, Harsh Vazirani, Anupam Shukla and Ritu Tiwari,( 2010), “Offline Handwriting Recognition using Genetic Algorithm”, IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 2, No 1.
[2] Chidambaran, C. and Lopes, H. S., (2010) ,"An Improved Artificial Bee Colony Algorithm for the Object Recognition Problem in Complex Digital Images Using Template Matching", International Journal of Natural Computing Research, Vol1, No.2.
[3] E. Elbeltagi, T. Hegazy, and D. Grierson,( 2005), “Comparison among Five Evolutionary-Based Optimization Algorithms” ,Advanced Engineering Informatics, Vol. 19, No. 1.
[4] T Hashni et al,( 2012 ),“Relative Study of CGS with ACO and BCO Swarm Intelligence Techniques” ,. IJCTA .Computer Technology & Applications, Vol. 3 No.5 .
[5] Elbeltagi, E., Hegazy, T., & Grierson, D. (2005), " Comparison Among Five Evolutionary-based Optimization Algorithms", Advanced Engineering Infrastructure, Vol.19 No.1.
[6] Yu, X. and Gen, M., (2010), "Introduction to Evolutionary Algorithms", Springer, London, UK.
[7]. E.Corchado,( 2006)," PSO and ACO in Optimization Problems" , Publishers : Springer - Verlag,.
[8] Yang Xiao, Xuemei Song and Zheng YAO. (2009), “improved Ant colony optimization with particle swarm optimization operator solving continuous optimization problems”, IEEE.
[9] Khan. K and Sahai. A. (2012)," A comparison of BA, GA, PSO, BP and LM for training feed forward neural networks in e-learning context", International Journal of Intelligent Systems and Applications (IJISA).Vol. 4, No.7.
[10] T Hashni et al,( 2012), “Relative Study of CGS with ACO and BCO Swarm Intelligence Techniques” ,Int Computer Technology & Applications, IJCTA ,Vol. 3 No.5 .
[11] Baijal A, Chauhan VS, Jayabarathi T (2011)," Application of PSO, artificial bee colony and bacterial foraging optimization algorithms to economic load dispatch", An analysis. Int. J. Computer Science, Vol.8, No.4.
[12] M. Dorigo and L. M. Gambardella, (1997),"The colony system: A cooperative learning approach to the traveling salesman problem", IEEE Transactions on Evolutionary Computation, Vol.1, No.1.
[13] M. Dorigo and G. Di Caro, (1999), “The Ant Colony Optimization meta-heuristic in New Ideas in ptimization",
[14]M. Dorigo and T. Stützle, (2002)," The ant colony optimization meta-heuristic: Algorithms, applications and advances", In F. Glover and G. Kochenberger editors, Handbook of Meta-heuristics, Vol. 57 of International Series in Operations Research & Management Science.
[15] Frans van den Bergh.,( 2001)," An Analysis of Particle Swarm Optimizers", PhD thesis, University of Pretoria,
[16] R. Sagayam1, Mrs. K. Akilandeswari,( 2012) “Comparison of Ant Colony and Bee Colony Optimization for Spam Host Detection”, International Journal of Engineering Research and Development ISSN: 2278-067X, 800X, Vol.4,No.8.
[17] Hemant Nagpure#, Rohit Raja, (2012),"The Applications Survey on Bee Colony Optimization",(IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 3,No.5.
[18] Dorigo M. (2010),"The ant system: optimization by a colony of cooperating agents". IEEE Trans Syst Man Cybern Part Byword Academy of Science, Engineering and Technology Vol.38 .

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