Paper title:

Personal Best Position Particle Swarm Optimization

Published in: Issue 1, (Vol. 6) / 2012
Publishing date: 2011-04-11
Pages: 69-76
Author(s): SINGH Narinder, SINGH S.B.
Abstract. In this paper, a new particle swarm optimization method has been proposed. In the proposed approach a novel philosophy of modifying the velocity update equation of Standard Particle Swarm Optimization approach has been used. The modification has been done by vanishing the best term in the velocity update equation of SPSO. The performance of the proposed algorithm (Personal Best Position Particle Swarm Optimization, PBPPSO) has been tested on several benchmark problems. It is concluded that the PBPPSO performs better than SPSO in terms of accuracy and quality of solution
Keywords: Standard Particle Swarm Optimization (SPSO), PBPPSO (Personal Best Position Particle Swarm Optimization), Gbest (Global Best Position), Pbest (Personal Best Position), Current Position
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, pp. 39–43, 1995.

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

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, vol. 3, pp. 1931–1938, July 1999.

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

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, pp. 235–242. IEEE Press, 2003.

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, vol.176, no. 8, pp.937–971, 2006.

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

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

12. P.J.Angline, “Evolutionary optimization versus particle swarm optimization philosophy and performance differences”, Lecture Notes in Computer Science, vol.1447, pp.601-610, Springer, Berlin, 1998a.

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

14. Z. Xinchao, “A perturbed particle swarm algorithm for numerical optimization”, Applied Soft Computing, pp. 119-124, 2010.

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

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

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

18. 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, pp.. 432-438, 2010.

19. 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, pp. 606-612,2010.

20. H. Liua, Z. Caia, and Y. Wang, “Hybridizing particle swarm Optimization with differential evolution for constrained numerical and engineering optimization”, Applied Soft Computing, pp. 629-640, 2010.

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

22. M.E.H. Pedersen, “Tuning & Simplifying Heuristical Optimization”, PhD thesis, School of Engineering Sciences, University of Southampton, England, 2010.

23. M.E.H. Pedersen and A.J. Chipper_eld, “Simplifying particle swarm optimization”, Applied Soft Computing, pp. 618-628, 2010.

24. A. Immanuel Selvakumar and K. Thanushkodi, “A New Particle Swarm Optimization Solution to Nonconvex Economic Dispatch Problems”, IEEE Transactions on Power Systems, vol. 22, no. 1, 2007.

25. Mehdi Neshat, Shima Farshchian Yazdi, “A New Cooperative Algorithm Based on PSO and K-Means for Data Clustering”, Journal of Computer Science vol. 8, no. 2, pp. 188-194, 2012

26. V.Krishna Reddy, L.S.S. Reddy, “ Performance Evaluation of Particle Swarm Optimization Algorithms on GPU using CUDA”,International Journal of Engineering Science & Advanced Technology, vol. 2, no. 1, pp.92-100, 2012.

27. Feng Luan, Jong-Ho Choi and Hyun – Kyo Jung, “A Particle Swarm Optimization Algorithm With Novel Expected Fitness Evaluation for Robust Optimization Problems”, IEEE Transactions on Magnetics, vol. 48, no. 2, 2012.

28. Hemlata S. Urda, and Rahila Patel, “Performance Evaluation of Dynamic Particle Swarm Optimization”, International Journal of Computer Science and Network, vol. 1, no.1, 2012.

29. Bahman Bahmanifirouzi, Mehdi Nafar and Masoud Jabbari, “Modified Particle Swarm Optimization for Economic Dispatch of Generating Units”, J. Basic, Applied Sci., vol.2, no.1, pp.138-140, 2012.

30. L.M.Palanivelu and P.Vijayakumar, “A Particle Swarm Optimization for Image Segmentation in Multi Application Smart Cards”, European Journal of Scientific Research, ISSN 1450-216X, vol. 70, no. 3,2012.

31. R. Mendes, J. Kennedy, and J. Neves, “The fully informed particle swarm: Simpler, maybe better,” IEEE Trans. Evol. Comput., vol. 8, pp.204–210, June 2004.

32. D.Bratton and James Kennedy, “Defining a Standard for Particle Swarm Optimization”,IEEE Swarm Intelligence Symposium, pp. 120-127, 2007.

33. Narinder Singh and S.B.Singh, “ One Half Global Best Position Particle Swarm Optimization Alogirthm”, International Journal of Scientific & Engineering Research vol. 2 no. 8, August, 2011.

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