An Improve Cuckoo Search Algorithm for Time-Table Problems

Jing Xing Fan; Yue Guang Li

doi:10.4028/www.scientific.net/AMR.1049-1050.1662

Paper Titles

Image Denoising Based on K-SVD with Adaptive Dictionary Size
p.1645

Three-Dimensional Simulation of Virtual Plants Branch Structure Based on Quasi Binary-Tree and L System
p.1650

Hyper-Parameters Selection of LS-SVM Based on PSO Algorithm with Multi-Particles Sharing Strategy
p.1654

Hopfield Neural Network Modeling Method Based on Subset KPCA with FCMC Clustering
p.1658

An Improve Cuckoo Search Algorithm for Time-Table Problems
p.1662

Recurrent Wavelet Neural Network Based on Immune Evolving Algorithm and its Application in Nonlinear Modeling
p.1666

An Improve Firefly Algorithm and its Application in Nonlinear Equation Groups
p.1670

MSRM Based Object Extraction Method for Image Sequences
p.1675

Logistics Distribution Route Optimization Based on Chaotic Cuckoo Algorithm
p.1681

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 1049-1050An Improve Cuckoo Search Algorithm for Time-Table...

An Improve Cuckoo Search Algorithm for Time-Table Problems

Abstract:

Time-table Problem of universities is a many factor of the global optimization problem. In this paper, according to the characteristics of time-table problem, an improve Cuckoo Search Algorithm was used to solve the Time-table Problem, adopting the code rule of randomized key representation based on the smallest position value, and then the design scheme of time-table problem of universities based on improved cuckoo search algorithm was expounded through studying influence factors of time-table problem of universities. Finally,the result shows the algorithm is feasible and effective.

You might also be interested in these eBooks

Modern Technologies in Materials, Mechanics and Intelligent Systems

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 1049-1050)

Pages:

1662-1665

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1049-1050.1662

Citation:

Cite this paper

Online since:

October 2014

Authors:

Jing Xing Fan*, Yue Guang Li

Keywords:

Ant Colony Algorithm, Cuckoo Search Algorithm, Intelligent Algorithm, TSP

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Garey M R, Johnson D S. Computers and Intractability: A Guide to the Theory of NP-Completeness[M]. San Franciso, CA: Freeman, 4, 109-122(1979).

Google Scholar

[2] Holland J H. Genetic Algorithms and the Optimal Allocation of Trials [J]. SIA M J Computer, 1973: 2 (2): 89-104.

Google Scholar

[3] Colorni A, Dorigo M, Maniezzo V. Distributed Optimization by Ant Colonies[A] . Proc 1st European Conf Artificial Life Plans[C]. France: Elsevier, 1991: 134-142.

Google Scholar

[4] Eberhart R, Kennedy J. A New Optimizer Using Particles Swarm Theory[A]. Proc 6th Int Symposium on Micro Machine and Human Science[C]. Nagoya: IEEE Service Center, Piscataway, 1995: 39-43.

Google Scholar

[5] Dasgupta D, Forrest S. Artificial Immune Systems and Their Applications [M]. Berlin: Spring-Verlag, 1998: 267-277.

Google Scholar

[6] Wang L. Intelligent Optimization Algorithms Applications [M]. Beijing: Tsinghua University Press, (2001).

Google Scholar

[7] Reingold. E. M. J. Neivergelt and N. Deo. Combinatorial Algorithms: Theory and practice Prentice-Hall , Englewood cliffs, NJ(1977).

Google Scholar

[8] Han K2H . Genetic quant um algorithm and its application to combinatorial optimization problem / / Proceedings of IEEE the 2000 Congress on Evolutionary Computation . San Diego , USA , IEEE Press , 2000 : 1354-1360.

Google Scholar

[9] Yang X S, Deb S. Cuckoo search via levy flights [C]. /Proceedings of world congress on nature & Biologically inspired computing, India IEEE Publications 2009: 210-214.

DOI: 10.1109/nabic.2009.5393690

Google Scholar