Abstract
This paper presents an improved particle swarm optimization algorithm (IPSO) for global numerical optimization. The IPSO uses more particles’ information to control the mutation operation. A new adaptive strategy for choosing parameters is also proposed to assure convergence of the IPSO. Meanwhile, we execute the IPSO to solve eight benchmark problems. The results show that the IPSO is superior to some existing methods for finding the best solution, in terms of both solution quality and algorithm robustness.
Keywords
- Particle Swarm Optimization
- Benchmark Function
- High Quality Solution
- Particle Swarm Optimization Method
- Standard Particle Swarm Optimization
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)
Mendes, R., Kennedy, J.: The fully informed particle swarm: simpler, maybe better. IEEE Trans. Evolutionary Computation. 3, 204–210 (2004)
Juang, C.F.: A hybrid of genetic algorithm and particle swarm optimization for recurrent network design. IEEE Trans. System, Man and Cybernetics-Part B 2, 997–1006 (2004)
van den Bergh, F., Engelbrecht, P.: A cooperative approach to particle swarm optimization. IEEE Trans. Evolutionary Computation 3, 225–239 (2004)
Janson, S., Middendorf, M.: A hierarchical particle swarm optimizer and its adaptive variant. IEEE Trans. System, Man and Cybernetics-Part B 6, 1272–1282 (2005)
Zhao, B., Guo, C.Y., Cao, Y.J.: A multiagent-based particle swarm optimization approach for optimal reactive power dispatch. IEEE Trans. Power Systems 2, 1070–1078 (2005)
Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann Publishers, San Francisco (2001)
Clerc, M., Kennedy, J.: The Particle Swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. on Evolutionary Computation 1, 58–73 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhao, B. (2006). An Improved Particle Swarm Optimization Algorithm for Global Numerical Optimization. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds) Computational Science – ICCS 2006. ICCS 2006. Lecture Notes in Computer Science, vol 3991. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11758501_88
Download citation
DOI: https://doi.org/10.1007/11758501_88
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34379-0
Online ISBN: 978-3-540-34380-6
eBook Packages: Computer ScienceComputer Science (R0)