Chaotic Cat Swarm Algorithms for Global Numerical Optimization

Shi Da Yang; Ya Lin Yi; Zhi Yong Shan

doi:10.4028/www.scientific.net/AMR.602-604.1782

Paper Titles

Numerical Simulation of Bionic Wing for Drag Reduction
p.1761

Numerical Simulation and Online Window Monitoring of SPR Process
p.1765

Numerical Simulation of Heat Flow in a Vehicle Cabin
p.1769

EBD Control Research on Nonuniformity Roads for Electric Vehicles on Energy Regenerative and Feedback Friction Integrated Braking
p.1775

Chaotic Cat Swarm Algorithms for Global Numerical Optimization
p.1782

Chaotic Grenade Explosion Algorithms for Global Numerical Optimization
p.1787

Homotopy-Inspired Cat Swarm Algorithm for Global Optimization
p.1793

Homotopy-Inspired Grenade Explosion Algorithm for Global Numerical Optimization
p.1798

Study on On the Cross-Sectional Shape Change of the Profiled Fiber along the Spinning Path in Melt Spinning
p.1804

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 602-604Chaotic Cat Swarm Algorithms for Global Numerical...

Chaotic Cat Swarm Algorithms for Global Numerical Optimization

Abstract:

A novel Chaotic Improved Cat Swarm Algorithm (CCSA) is presented for global optimization. The CSA is a new meta-heuristic optimization developed based on imitating the natural behavior of cats and composed of two sub-models: tracing mode and seeking mode, which model upon the behaviors of cats. Here different chaotic maps are utilized to improve the seeking mode step of the algorithm. Seven different chaotic maps are investigated and the Logistic and Sinusoidal maps are found as the best choices. Comparing the new algorithm with the CSA method demonstrates the superiority of the CCSA for the benchmark functions.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 602-604)

Pages:

1782-1786

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.602-604.1782

Citation:

Cite this paper

Online since:

December 2012

Authors:

Shi Da Yang*, Ya Lin Yi, Zhi Yong Shan

Keywords:

Cat Swarm Algorithm, Chaos, Global Optimization, Meta-Heuristic Algorithms

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Shu-Chuan Chu, Pei-wei Tsai, Jeng-Shyang Pan. Computational intelligence based on the behavior of cats. International Journal of Innovative Computing, Information and Control, Vol.3, 2007,p.163–173.

Google Scholar

[2] Shyan-Shiou Chen. Chaotic Simulated Annealing by a Neural Network with a Variable Delay: Design and Application. IEEE transactions on neural networks, Vol.22, No.10, october 2011,pp.1557-1565.

DOI: 10.1109/tnn.2011.2163080

Google Scholar

[3] Lin Zhou, Yan Chen, Ke Guo, Fangcheng Jia. New Approach for MPPT Control of Photovoltaic System With Mutative-Scale Dual-Carrier Chaotic Search. IEEE transactions on power electronics, Vol. 26, No. 4, april 2011.pp.1038-1048.

DOI: 10.1109/tpel.2010.2078519

Google Scholar

[4] Bilal Alatas. Chaotic bee colony algorithms for global numerical optimization, Expert Systems with Applications, Vol.37, 2010,p.5682–5687.

DOI: 10.1016/j.eswa.2010.02.042

Google Scholar

[5] Irina Ciornei, and Elias Kyriakides. Hybrid Ant Colony-Genetic Algorithm (GAAPI)for Global Continuous Optimization. IEEE transactions on systems,man,and cybernetics-part B:cybernetics, accepted.

DOI: 10.1109/tsmcb.2011.2164245

Google Scholar

[6] Wei-Chang Yeh. Optimization of the Disassembly Sequencing Problem on the Basis of Self-Adaptive Simplified Swarm Optimization. IEEE transactions on systems,man,and cybernetics-part A: systems and humans, Vol. 42, No. 1,January 2012.pp.250-261.

DOI: 10.1109/tsmca.2011.2157135

Google Scholar