An Efficiency Algorithm for SDCP Program

Ning Xiao

doi:10.4028/www.scientific.net/AMR.971-973.1533

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 971-973An Efficiency Algorithm for SDCP Program

An Efficiency Algorithm for SDCP Program

Abstract:

For more effectively solving SDCP,in the paper,using BP neural networks to approximate chance function,training samples are produced by random simulation,and a hybrid intelligent algorithm for SDCP combined stochastic particle swarm optimization and BP neural network is proposed.The experimental results show that the algorithm is more preferable.

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Periodical:

Advanced Materials Research (Volumes 971-973)

Pages:

1533-1536

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.971-973.1533

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Online since:

June 2014

Authors:

Ning Xiao*

Keywords:

NNs, SDCP, SPSO

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* - Corresponding Author

References

[1] J. kennedy,R. Eberhart. particle swarm optimization. in proc. International conference on Neural Networks. perth, WA, Australia(1995)p.1942-(1948).

Google Scholar

[2] J.C. Zeng Z.H. Cui and D. Jie, Particle Swarm Optimization, Science publishing company, Bei Jing (2004).

Google Scholar

[3] X. S GE, K. Sun. Optimal control of a spacecraft with deployable solar arrays using particle swarm optimization algorithm. Science China(Technological Sciences. Vols. 54(5) (2011)pp.1107-1112.

DOI: 10.1007/s11431-011-4350-z

Google Scholar

[4] Z. x. Zhang,H. q. Cui. Localization in 3D Sensou Netwourks Using Stochastic particle Swarm Optimization. Wuhan University Journal of Natural Science. 17(6) (2012)pp.544-548.

DOI: 10.1007/s11859-012-0884-6

Google Scholar

[5] B.J. Liu. Dependent-chance programming: A class of stochastic programming. Computers& Mathematics with Applications. Vols. 34(12) (1997)pp.89-104.

DOI: 10.1016/s0898-1221(97)00237-x

Google Scholar

[6] B.J. Liu R.Q. Zhao and G. Wang, Theory and Practice of Uncertain Programming, Tsinghua University publishing company, Beiing, (2003).

Google Scholar

[7] R. q. Zhao. Uncertain Programming: Present and Future. 6th National Conference of Operations Research Society of China. HongKong: Global-Link publishing company(2000) pp.255-263.

Google Scholar

[8] J. Peng B.J. Liu. Uncertain Programming: Current Status and Future Prospects, Operations Research And Management Science, Vols. 11(2) (2002) pp.1-10.

Google Scholar

[9] P. z. Guo, Method research on vehicle navigation path planning under random environment, China Master's Theses Full-text Database(2012).

Google Scholar

[10] J.C. Zeng, Z.H. Cui. Guaranteed Global Convergence Particle Swarm Optimizer. Journal Of Computer Research And Development, Vols. 41(8)(2004)pp.1333-1338.

Google Scholar

[11] N. Xiao. Based Stochastic Particle Swarm Optimization and Random Simulation for Solving StochasticDependent-Chance Programming. Applied Mechanics and Materials Vols. 499 (2013) p.1960-(1964).

DOI: 10.4028/www.scientific.net/amm.496-500.1960

Google Scholar