Abstract
Particle Swarm Optimizer (PSO) is one of the evolutionary computation techniques based on swarm intelligence. Comprehensive Learning Particle Swarm Optimizer (CLPSO) is a variant of the original Particle Swarm Optimizer which uses a new learning strategy to make the particles have different learning exemplars for different dimensions. This paper investigates the effects of learning proportion P c in the CLPSO, showing that different P c realizes different performance on different problems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: P. 6th Int. Symposium on Micromachine and Human Science, Nagoya, Japan, pp. 39–43 (1995)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: P. of IEEE International Con- ference on Neural Networks, Piscataway, NJ, pp. 1942–1948 (1995)
Shi, Y., Eberhart, R.C.: A modified particle swarm optimizer. In: Proc. of the IEEE Con- gress on Evolutionary Computation (CEC 1998), Piscataway, NJ, pp. 69–73 (1998)
Ratnaweera, A., Halgamuge, S., Watson, H.: Self-organizing hierarchical particle swarm optimizer with time varying accelerating coefficients. IEEE Transactions on Evolutionary Computation 8, 240–255 (2004)
Kennedy, J.: Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In: P. CEC, Washington DC, pp. 1931–1938 (1999)
Kennedy, J., Mendes, R.: Population structure and particle swarm performance. In: Proc. of the CEC 2002, Honolulu, Hawaii USA (2002)
Suganthan, P.N.: Particle swarm optimiser with neighborhood operator. In: P. of Congress on Evolutionary Computation, Washington DC, pp. 1958–1962 (1999)
van den Bergh, F., Engelbrecht, A.P.: A cooperative approach to particle swarm optimization. IEEE Trans. on Evolutionary Computation 8, 225–239 (2004)
Lovbjerg, M., Rasmussen, T.K., Krink, T.: Hybrid particle swarm optimiser with breeding and subpopulations. In: P. of Genetic and Evolutionary Computation Conf. (2001)
Peram, T., Veeramachaneni, K., Mohan, C.K.: Fitness-distance-ratio based particle swarm optimization. In: P. IEEE Swarm Intelligence Sym., Indiana, USA, pp. 174–181 (2003)
Liang, J.J., Qin, A.K., Suganthan, P.N., Baskar, S.: Particle swarm optimization algorithms with novel learning strategies. In: P. IEEE Int. Conf. on Systems Man and Cybernetics, The Netherlands (October 2004), http://www.ntu.edu.sg/home/EPNSugan/
Salomon, R.: Reevaluating genetic algorithm performance under coordinate rotation of benchmark functions. BioSystems 39, 263–278 (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liang, J.J., Qin, A.K., Suganthan, P.N., Baskar, S. (2004). Evaluation of Comprehensive Learning Particle Swarm Optimizer. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_34
Download citation
DOI: https://doi.org/10.1007/978-3-540-30499-9_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23931-4
Online ISBN: 978-3-540-30499-9
eBook Packages: Springer Book Archive