Robust Optimization Based on an Improved Genetic Algorithm

Le Wei Yan; Zuo Yu Sun; Ke Yang Mao

doi:10.4028/www.scientific.net/AMR.655-657.955

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 655-657Robust Optimization Based on an Improved Genetic...

Robust Optimization Based on an Improved Genetic Algorithm

Abstract:

An improved genetic algorithm is applied to solve the problem of the robust optimization for structures under complicated loading. The objective function is constructed by signal-to-noise ratio of Taguchi target. Numerical examples demonstrated that the improved genetic algorithm combined with the robust design method can effectively solve the problem of the robust optimization for structures under complicated loading with uncertain parameters.

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Periodical:

Advanced Materials Research (Volumes 655-657)

Pages:

955-958

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.655-657.955

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Online since:

January 2013

Authors:

Le Wei Yan, Zuo Yu Sun, Ke Yang Mao

Keywords:

Genetic Algorithm (GA), Robust Optimization, Signal-to-Noise Ratio (SNR), Taguchi Target

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References

[1] Nishida N, Takahashi Y, Wakao S. Robust design optimization approach by combination of sensitivity analysis and sigma level estimation, IEEE Transaction on magnetics, 2008, 44(6): 998-1001.

DOI: 10.1109/tmag.2007.915329

Google Scholar

[2] Leung S C H, Tsang S O S, Ng W L , Wu Y. A robust optimization model for multi-site production planning problem in an uncertain environment. European Journal of Operational Research, 2007, 181(1): 224-238.

DOI: 10.1016/j.ejor.2006.06.011

Google Scholar

[3] Qiu Z P, Hu J X, Yang H L, Lu Q S. Exact bounds for the sensitivity analysis of structures with uncertain-but-bounded parameters Applied Mathematical Modelling, 2008, 32(6): 1143-1157.

DOI: 10.1016/j.apm.2007.03.004

Google Scholar

[4] Mohamed J A H, Sivakumar R. A survey: hybrid evolutionary algorithms for cluster analysis. Artificial Intelligence Review, 2011: 1-26.

Google Scholar

[5] Gunawan S, Azarm S. Multi-objective robust optimization using a sensitivity region concept. Structure and Multidisciplinary Optimization, 2005, 29(1): 50-60.

DOI: 10.1007/s00158-004-0450-8

Google Scholar

[6] Burcin C, Fulya A, Berna D. Multi-objective optimization of a stochastic assembly line balancing: A hybrid simulated annealing algorithm. Computers and Industrial Engineering, 2011, 60(3): 376-384.

DOI: 10.1016/j.cie.2010.08.013

Google Scholar