Abstract
Evolving artificial neural network is an important issue in both evolutionary computation (EC) and neural networks (NN) fields. In this paper, a hybrid particle swarm optimization (PSO) is proposed by incorporating differential evolution (DE) and chaos into the classic PSO. By combining DE operation with PSO, the exploration and exploitation abilities can be well balanced, and the diversity of swarms can be reasonably maintained. Moreover, by hybridizing chaotic local search (CLS), DE operator and PSO operator, searching behavior can be enriched and the ability to avoid being trapped in local optima can be well enhanced. Then, the proposed hybrid PSO (named CPSODE) is applied to design multi-layer feed-forward neural network. Simulation results and comparisons demonstrate the effectiveness and efficiency of the proposed hybrid PSO.
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
Yao, X.: Evolving Artificial Neural Networks. Proceedings of the IEEE 87, 1423–1447 (1999)
Wang, L.: Intelligent Optimization Algorithms with Applications. Tsinghua University & Springer Press, Beijing (2001)
Kennedy, J., Eberhart, R.C., Shi, Y.: Swarm Intelligence. Morgan Kaufmann Publishers, San Francisco (2001)
Storn, R., Price, K.: Differential Evolution: A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces. Technical report, TR-95-012, International Computer Science Institute, Berkley (1995)
Li, B., Jiang, W.: Optimizing Complex Functions by Chaos Search. Cybernetics and Systems 29, 409–419 (1998)
Wang, L., Zheng, D.Z., Lin, Q.S.: Survey on Chaotic Optimization Methods. Computing Technology and Automation 20, 1–5 (2001)
Blake, C., Keogh, E., Merz, C.J.: UCI Repository of Machine Learning Databases, University of California, Irvine, Dept. of Information and Computer Sciences (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, B., Wang, L., Jin, Y., Huang, D. (2005). Designing Neural Networks Using Hybrid Particle Swarm Optimization. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_62
Download citation
DOI: https://doi.org/10.1007/11427391_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25912-1
Online ISBN: 978-3-540-32065-4
eBook Packages: Computer ScienceComputer Science (R0)