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 nonscalable 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 MegaMinds:
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.601610, Springer, Berlin.
[13] Angline, P.J ‘Using selection to improve
particle swarm optimization’, Proceedings of
the IEEE Conference on Evolutionary
Computations, 1998b pp.8489.
[14] Clerc M., Kennedy J., “ The Particle Swarm :
Explosion, Stability, and Convergence in a
Multidimensional Complex Space”, IEEE
Transactions on Evolutionary
Computation,Vol.6, 2002, pp 5873.
[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.
8488. San Diego, CA.
[16] ZH. Zhan, J. Zhang, Y. Li, and H.SH.
Chung. Adaptive particle swarm
optimization. IEEE Transactions on Systems,
Man, and Cybernetics, 2009, pp 13621381.
[17] Z. Xinchao. A perturbed particle swarm
algorithm for numerical optimization.
Applied Soft Computing, 2010, pp 119124.
[18] T. Niknam and B. Amiri. An efficient hybrid
approach based on PSO,ACO and kmeans
for cluster analysis. Applied Soft Computing,
2010, pp 183 197.
[19] M. ElAbda, H. Hassan, M. Anisa, M.S.
Kamela, and M. Elmasry. Discrete
cooperative particle swarm optimization for
FPGA placement. Applied Soft Computing,
2010 pp 284295.
[20] MR. Chena, X. Lia, X. Zhanga, and YZ. Lu.
A novel particle swarm optimizer hybridized
with extremal optimization. Applied Soft
Computing, 2010, pp 367373.
[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
432438.
[22] CC. Hsua, WY. Shiehb, and CH. Gao.
Digital redesign of uncertain interval systems
based on extremal gain/phase margins via a
hybrid particle swarm optimizer. Applied
Soft Computing, 2010, pp 606612.
[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 629640.
[24] K. Mahadevana and P.S. Kannan.
Comprehensive learning particle swarm
optimization for reactive power dispatch.
Applied Soft Computing, 2010, pp 641652.
[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 618628.


