The Novel Compound Evolutionary Optimization Algorithm with Hybrid Discrete Variables and its Application to Mechanical Optimization

You Xin Luo

doi:10.4028/www.scientific.net/AMR.97-101.3276

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 97-101The Novel Compound Evolutionary Optimization...

The Novel Compound Evolutionary Optimization Algorithm with Hybrid Discrete Variables and its Application to Mechanical Optimization

Abstract:

To overcome the problem of low convergence speed and sensitivity to local convergence with the traditional Artificial Fish-swarm Algorithm (AFSA) to handle complex functions, a novel compound evolutionary algorithm, called AFS-EMPCEOA, was introduced which is combined Artificial Fish-swarm Algorithm with the Elite Multi-parent Crossover Evolutionary Optimization Algorithm (EMPCEOA) that is GuoTao Algorithm improved by elite multi-parent crossover method. AFSEMPCEOA algorithm program with hybrid discrete variables was also developed. The computing example of mechanical optimization design shows that this algorithm has no special requirements on the characteristics of optimal designing problems, which has a fairly good universal adaptability and a reliable operation of program with a strong ability of overall convergence and high efficiency.

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Periodical:

Advanced Materials Research (Volumes 97-101)

Pages:

3276-3280

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.97-101.3276

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Online since:

March 2010

Authors:

You Xin Luo

Keywords:

Artificial Fish-Swarm Algorithm (AFSA), Elite Multi-Parent Crossover, GuoTao Algorithm, Hybrid Discrete Variable, Mechanical Optimisation

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References

[1] H.X. Guo, L.T. Zhang: Journal of Machine Design, Vol. 22, No. 3(2005), pp.9-13.

Google Scholar

[2] Y. M Zhou, Y.M. Lan: Optimization Design Modeling of Mechanical Parts and System and its Application (Chemical Industry Press, Beijing 2005).

Google Scholar

[3] T. Guo, L.S. Kang, Y. Li: Journal of Wuhan University(Natural Science Edition), Vol. 45, No. 5B (1999), pp.771-775.

Google Scholar

[4] X.Y. Che, Y.X. Luo, Z.M. He: Machine Tool & Hydraulics, Vol. 37, No. 3(2009), pp.24-26.

Google Scholar

[5] H.J. Huang, Y.Q. Zhou: Journal of Computer Applications, Vol. 28, No. 12(2008), pp.3062-3064.

Google Scholar

[6] Z.J. Wu, L. S Kang , X.F. Zou: Computer Applications, Vol. 23, No. 2(2003), pp.13-15.

Google Scholar

[7] Z. Zhang, Q.C. Meng, R. Xue,: Journal of Harbin Institute of Technology, Vol. 38, No. 10(2006), pp.1716-1718.

Google Scholar

[8] Y.X. Luo, D.G. Liao, X. Y. Che: Transactions of the Chinese Society of Agricultural, Vol. 39, No. 9(2008), pp.129-133.

Google Scholar

[9] D. Y. Xue, Y. Q. Chen: MATLAB Solving of Advanced Applied Mathematics Problem (Tsinghua University Press, Beijing 2008).

Google Scholar

[10] J. K. Xu: Mechanical Optimization Design (China Machine Press, Beijing 1996).

Google Scholar