Robust Design Optimization Based on Improved Ant Colony Algorithm with Application

Shi Ming Hao; Hui Xin Guo; Juan Dai; Li Zhi Cheng

doi:10.4028/www.scientific.net/AMM.29-32.2273

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 29-32Robust Design Optimization Based on Improved Ant...

Robust Design Optimization Based on Improved Ant Colony Algorithm with Application

Abstract:

The basic ant colony algorithm is improved by introducing chaos model of Logistic and discrete search in order to improve the global convergence. A program of improved ant colony algorithm has been designed by using Matlab. It can be used for solving optimal design problems with continuous variables, discrete variables or mixed-discrete variables. For an electric circuit, the design problem about the robustness of electric current is discussed and a model of robust optimal design is established. The solution of established model is achieved by using the proposed method and the robustness of the electric circuit has been improved. The example shows that the proposed method is effective in engineering design.

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Periodical:

Applied Mechanics and Materials (Volumes 29-32)

Pages:

2273-2277

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.29-32.2273

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Online since:

August 2010

Authors:

Shi Ming Hao, Hui Xin Guo, Juan Dai, Li Zhi Cheng

Keywords:

Ant Colony Algorithm (ACA), Electric Circuit, Optimization Design, Robust Design

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References

[1] M. Dorigo, V. Maniezzo and A. Colorni. Ant system: Optimization by colony of cooperating agents. IEEE Transactions on System, Man and Cybernetics (Part B). Vol. 26, Issue 1 (1996), pp.29-41.

DOI: 10.1109/3477.484436

Google Scholar

[2] Li Shiyong. Progresses in ant colony optimization algorithm with applications. Computer Automated Measurement & Control. Vol. 11, Issue 12 (2003), p.911－913, 917.

Google Scholar

[3] G. Taguchi. Introduction to quality engineering: designing quality into products and processes. Tokyo: Asian Productivity Organization (1986).

Google Scholar

[4] Guo Huixin, Zhang Longting. Compound genetic algorithm on optimization design of hybrid discrete variables. Machine Design. Vol. 22, Issue 3 (2005), pp.9-11.

Google Scholar

[5] Z. Michelewicz. Genetic algorithm + data structure = evolutionary programs. 3th ed. New York: Springer Verlag (1996).

Google Scholar

[6] Huixin Guo, Hongzhong Huang and Liang Liang. Fuzzy-robust design optimization with multiquality characteristics. Engineering Optimization. Vol. 42, Issue 1 (2010), pp.87-99.

DOI: 10.1080/03052150903089690

Google Scholar

[7] Guo Huixin. Study on the fuzzy robustness of product quality and method of fuzzy robust optimal design. China Mechanical Engineering. Vol. 13, Issue 3 (2002), pp.221-224.

Google Scholar

[8] Chen Lizhou. Robust design. Beijing: Mechanical Industry Publishing House (2000).

Google Scholar

[9] A. Parkinson. Robust mechanical design using engineering models. Tran. of the ASME. J. of Mech. Design. Vol. 117, Issue B (1995), pp.48-54.

Google Scholar