Abstract
Particle swarm optimization (PSO) has been shown to perform well on many optimization problems. However, the PSO algorithm often can not find the global optimum, even for unimodal functions. It is necessary to study the local search ability of PSO. The interval compression method and the probabilistic characteristic of the searching interval of particles are used to analyze the local search ability of PSO in this paper. The conclusion can be obtained that the local search ability of a particle is poor when the component of the global best position lies in between the component of the individual best position and the component of the current position of the particle. In order to improve the local search ability of PSO, a new learning strategy is presented to enhance the probability of exploitation of the global best position. The experimental results show that the modified PSO with the new learning strategy can improve solution accuracy.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceeding of International Conference on Neural Networks, pp. 1942–1948. IEEE Press, Perth (1995)
Shi, Y.H., Eberhart, R.C.: A Modified Particle Swarm Optimizer. In: Proceeding of IEEE Congress on evolutionary Computation, pp. 69–73. IEEE Press, Piscataway (1998)
Asanga, R., Saman, K.H., Harry, C.W.: Self-organizing Hierarchical Particle Swarm Optimizer with Time-varying Acceleration Coefficients. IEEE Transaction on evolutionary computation 8(3), 240–255 (2004)
Kennedy, J.: Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In: Proceeding of IEEE Congress on evolutionary Computation, pp. 1931–1938. IEEE Press, Washington (1999)
Arumugam, M.S., Rao, M.V.C., Tan Alan, W.C.: A Novel and Effective Particle Swarm Optimization Like Algorithm with Extrapolation Technique. Applied soft computing 9, 308–320 (2009)
Liang, J.J., Qin, A.K., Suganthan, P.N.: Comprehensive Learning Particle Swarm Optimizer for Global Optimization of Multimodal Functions. IEEE Transaction on evolutionary computation 10(3), 281–295 (2006)
Zhan, Z.H., Zhang, J., Li, Y.: Adaptive Particle Swarm Optimization. IEEE Transaction on systems, man and cybernetics-part B 39(6), 1362–1381 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shen, Y., Wang, G. (2010). Study on the Local Search Ability of Particle Swarm Optimization. In: Tan, Y., Shi, Y., Tan, K.C. (eds) Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science, vol 6145. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13495-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-13495-1_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13494-4
Online ISBN: 978-3-642-13495-1
eBook Packages: Computer ScienceComputer Science (R0)