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International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 8    
Website: http://www.ijser.org
scirp IJSER >> Volume 2, Issue 8, August 2011
One Half Global Best Position Particle Swarm Optimization Algorithm
Full Text(PDF, 3000)  PP.  
Author(s)
Narinder Singh, S.B.Singh
KEYWORDS
Particle Swarm Optimization, One Half Global Best Position Particle Swarm Optimization, Personal Best Position, Global Best Position, Global optimization, Velocity update equation
ABSTRACT
In this paper, derived a new particle swarm optimization algorithm, called OHGBPPSO (One Half Personal Best Position Particle Swarm Optimizations), is presented, and based on a novel philosophy by modifying the velocity update equation. Its performance based on numerical and graphical analyses of results is compared with the standard PSO (SPSO) and One Half Global Best Position Particle Swarm Optimization by scalable and non-scalable problems.
References
[1] R.C. Eberhart and J. Kennedy A New Optimizer using Particle Swarm Theory. In Proceedings of the Sixth International Symposium on Micromachine and Human Science, 1995, pp 39–43.

[2] J. Kennedy and R.C. Eberhart. Particle Swarm Optimization. In Proceedings of the IEEE International Joint Conference on Neural Networks, 1995, pp 1942–1948. IEEE Press.

[3] J. Kennedy. Small Worlds and Mega-Minds: Effects of Neighborhood Topology on Particle Swarm Performance. In Proceedings of the IEEE Congress on Evolutionary Computation, volume 3, July 1999, pages 1931–1938.

[4] J. Kennedy and R. Mendes Population Structure and Particle Performance. In Proceedings of the IEEE Congress on Evolutionary Computation, 2002. pages 1671– 1676. IEEE Press.

[5] E.S. Peer, F. van den Bergh, and A.P. Engelbrecht. Using Neighborhoods with the Guaranteed Convergence PSO. In Proceedings of the IEEE Swarm Intelligence Symposium, 2003, pp 235–242. IEEE Press.

[6] A.P. Engelbrecht. Fundamentals of Computational Swarm Intelligence. Wiley & Sons, 2005.

[7] J. Kennedy, R.C. Eberhart, and Y. Shi. Swarm Intelligence. Morgan Kaufmann, 2001.

[8] F. van den Bergh An Analysis of Particle Swarm Optimizers. PhD thesis, Department of Computer Science, University of Pretoria, Pretoria, South Africa, 2002.

[9] F. van den Bergh and A.P. Engelbrecht. A Study of Particle Swarm Optimization Particle Trajectories. Information Sciences, 176(8) , 2006, pp 937–971.

[10] J. Kennedy. Bare Bones Particle Swarms. In Proceedings of the IEEE Swarm Intelligence Symposium, April 2003, pp 80–87.

[11] Y. Shi and R.C. Eberhart. A Modified Particle Swarm Optimizer. In Proceedings of the IEEE Congress on Evolutionary Computation, May 1998, pp 69–73.

[12] Angline, P.J. ‘Evolutionary optimization versus particle swarm optimization philosophy and performance differences’, Lecture Notes in Computer Science, Vol.1447, 1998a pp.601-610, Springer, Berlin.

[13] Angline, P.J ‘Using selection to improve particle swarm optimization’, Proceedings of the IEEE Conference on Evolutionary Computations, 1998b pp.84-89.

[14] Clerc M., Kennedy J., “ The Particle Swarm : Explosion, Stability, and Convergence in a Multi-dimensional Complex Space”, IEEE Transactions on Evolutionary Computation,Vol.6, 2002, pp 58-73.

[15] Eberhart, R. C. and Shi, Y. Comparing inertia weigthts and constriction factors in particle swarm optimization. Proceedings of IEEE Congress on Evolutionary Computation, 2000 pp. 84-88. San Diego, CA.

[16] Z-H. Zhan, J. Zhang, Y. Li, and H.S-H. Chung. Adaptive particle swarm optimization. IEEE Transactions on Systems, Man, and Cybernetics, 2009, pp 1362-1381.

[17] Z. Xinchao. A perturbed particle swarm algorithm for numerical optimiza-tion. Applied Soft Computing, 2010, pp 119-124.

[18] T. Niknam and B. Amiri. An efficient hybrid approach based on PSO,ACO and k-means for cluster analysis. Applied Soft Computing, 2010, pp 183- 197.

[19] M. El-Abda, H. Hassan, M. Anisa, M.S. Kamela, and M. Elmasry. Discrete cooperative particle swarm optimization for FPGA placement. Applied Soft Computing, 2010 pp 284-295.

[20] M-R. Chena, X. Lia, X. Zhanga, and Y-Z. Lu. A novel particle swarm optimizer hybridized with extremal optimization. Applied Soft Computing, 2010, pp 367-373.

[21] P.W.M. Tsang, T.Y.F. Yuena, and W.C. Situ. Enhanced a_ne invariant matching of broken boundaries based on particle swarm optimization and the dynamic migrant principle. Applied Soft Computing, 2010, pp 432-438.

[22] C-C. Hsua, W-Y. Shiehb, and C-H. Gao. Digital redesign of uncertain interval systems based on extremal gain/phase margins via a hybrid particle swarm optimizer. Applied Soft Computing, 2010, pp 606-612.

[23] H. Liua, Z. Caia, and Y. Wang. Hybridizing particle swarm Optimi- -zation with differential evolution for constrained numerical and engineering optimization. Applied Soft Computing, 2010, pp 629-640.

[24] K. Mahadevana and P.S. Kannan. Comprehensive learning particle swarm optimization for reactive power dispatch. Applied Soft Computing, 2010, pp 641-652.

[25] M.E.H. Pedersen. Tuning & Simplifying Heuristical Optimization. Ph.D. thesis, School of Engineering Sciences, University of Southampton, England, 2010.

[26] M.E.H. Pedersen and A.J. Chipper_eld. Simplifying particle swarm optimization. Applied Soft Computing, 2010, pp 618-628.

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