Abstract
On the basis of establishment of supply chain optimization model and particle swarm optimization algorithm, an improved particle swarm optimization algorithm is proposed in this paper to solve supply chain optimization problem. In the optimization process, the improved algorithm replaced part of poor fitness value particles to fit fine fitness value particles, so the algorithm has filtering capability, which can speedup the search course and ensure the convergence in global optimal solution. Experimental results are validated and compared with particle swarm algorithm, which indicating that the improved particle swarm algorithm has better performance, and it has simpler, faster and more accurate features.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Peng, Z.: Supply chain partners game analysis and evaluation selection. Intelligence Journal 2, 59–60 (2009)
Ip, W.H., Huang, M., Yung, K.L., Wang, D.: Genetic algorithm solution for a risk-based partner selection problem in a virtual enuerprise. Computers & Operations Research 30, 213–231 (2003)
Cao, H.Y., Wang, D.W.: A simulation based genetic algorithm for risk-based partner selection in new product development. International Journal of Industrial Engineering 10(1), 16–25 (2009)
Wu, N.Q., Su, P.: Selection of partners in virtual enterprise paradig. Robotics and Computer-Integrated Manufacturing 21, 119–131 (2009)
Kennedy, J., et al.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE Service Center, Australia (1995)
Naka, S., Genji, T., Yura, T., Fukuyama, Y.: A hybrid particle swarm optimization for distribution state estimation. IEEE Trans. on Power Systems 18(1), 60–68 (2003)
Gaing, Z.L.: Discrete particle swarm optimization algorithm for unit commitment. In: Proceeding of IEEE Power Engineering Society General Meeting, Toronto, Ontario, Canada, vol. 1, pp. 418–424 (2003)
Carlisle, A., Dozier, G.: An off-the-shelf PSO. In: Proceedings of the Particle Swarm Optimization Workshop, pp. 1–6 (2010)
Wang, F., Wang, Z., Wang, S.: A dynamic inertia weight particle swarm optimization. China Mechanical Engineering 16(11), 945–948 (2005)
Wang, J., Wang, D.: PSO inertia weight in the experiment and analysis. Systems Engineering 20(2), 194–198 (2005)
Liu, Z., Zhang, J.: Substation Locating and Sizing Distribution Network based on Multi-organization improved particle swarm optimization algorithm. In: Proceedings of the CSEE, vol. 27(1), pp. 105–111 (2009)
Xu, K., Liu, D.: Coevolutionary particle swarm algorithm. Computer Engineering and Applications 45(3), 51–54 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wei, X. (2011). Supply Chain Optimization Based on Improved PSO Algorithm. In: Liu, C., Chang, J., Yang, A. (eds) Information Computing and Applications. ICICA 2011. Communications in Computer and Information Science, vol 244. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27452-7_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-27452-7_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27451-0
Online ISBN: 978-3-642-27452-7
eBook Packages: Computer ScienceComputer Science (R0)