Multi-Objective Optimal Design of Frame Structures with Group Search Optimizer

Feng Liu; Li Juan Li; Bing Yuan

doi:10.4028/www.scientific.net/AMM.121-126.968

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 121-126Multi-Objective Optimal Design of Frame Structures...

Multi-Objective Optimal Design of Frame Structures with Group Search Optimizer

Abstract:

Multi-objective optimal design of frame structures was carried out by group search optimizer with Pareto solutions theory in this paper. A one bay frame structure was employed to evaluate the performance of group search optimizer. Calculation results show the stochastic algorithm has excellent performance in terms of convergence rate, feasibility, practicality and superiority in structure optimal design.

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Periodical:

Applied Mechanics and Materials (Volumes 121-126)

Pages:

968-975

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.121-126.968

Citation:

Cite this paper

Online since:

October 2011

Authors:

Feng Liu, Li Juan Li, Bing Yuan

Keywords:

Frame Structure, Group Search Optimizer, Multi-Objective Optimization (MOP)

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References

[1] Cui X. X: National Defense Industry Press. (2006).

Google Scholar

[2] Knowles J.D., Come D. W: Evolutionary Computation Vol. 8 (2000), p.149.

Google Scholar

[3] Shin S. Y, Lee I. H., Kin D: IEEE Transactions on Evolutionary Computation Vol. 9 (2005), p.143.

Google Scholar

[4] Deb K., Agrawal S., Pratba A: IEEE Transactions on Evolutionary Computation Vol. 6 (2002), p.182.

Google Scholar

[5] Osman M.S., ABO-Sinna M.A., Mousa A. A., IT-CEMOP: Applied Mathematics and Computation Vol. 18 (2006), p.373.

DOI: 10.1016/j.amc.2006.05.095

Google Scholar

[6] Zhou Z., Dai G.M., Fang P., Chen F.J. and Tan Y: Springer Berlin / Heidelberg. (2008).

Google Scholar

[7] S.L. Ho, Yang S.Y., Ni G.Z., Lo Edward W.C., and Wong H. C: IEEE Transactions on Magnetics Vol. 41 (2005), p.1756.

Google Scholar

[8] Baumgartner U., Magele Ch., and Renhart W: IEEE Transactions on Magnetics Vol. 40 (2004), p.1172.

Google Scholar

[9] Zhang W.B., Lai L. F: Journal of Guangxi University (Natural Science Edition) Vol. 33 (2008), p.40(in Chinese).

Google Scholar

[10] He S., Wu Q.H. and Saunder J. R, in: IEEE Congress on Evolutionary Computation. Vancouver, BC, Canada (2006).

Google Scholar

[11] Barnard C J, Sibly R M: Animal Behavior Vol. 29 (1981), p.543.

Google Scholar

[12] An W.G., Li W. J: Journal of Northwestern Polytechnical University Vol. 22 (2004), p.563.

Google Scholar

[13] Wen-Shan Chuang: Master's Thesis of National Taiwan University (2007).

Google Scholar