Joint Optimization Combining Genetic Algorithm and Numerical Algorithm Based on MATLAB

Yan Hua Guo; Fei Fei Liu; Ning Zhang; Tao Wang

doi:10.4028/www.scientific.net/AMR.1065-1069.3442

Paper Titles

Wind Turbine Grounding Resistance Numerical Calculation
p.3414

A New Variant Harmony Search Algorithm with Random Mutation Strategy
p.3425

Data Storage Architecture of Maintenance Support System Based on Hadoop
p.3429

Harmony Search Algorithm with Opposition-Based Learning for Power System Economic Load Dispatch
p.3434

Hybrid Harmony Search Algorithm for Global Optimization Problems
p.3438

Joint Optimization Combining Genetic Algorithm and Numerical Algorithm Based on MATLAB
p.3442

The Application of the CORS in the Research of Digital Urban Municipal Supervision and Management
p.3446

The Equilibrium Existence for Constrained Multiobjective Games in GFC-Spaces
p.3450

Crane Location Optimization Method for Substation Construction
p.3455

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 1065-1069Joint Optimization Combining Genetic Algorithm and...

Joint Optimization Combining Genetic Algorithm and Numerical Algorithm Based on MATLAB

Abstract:

The mathematic model of a two-bar truss is built in MATLAB and the analysis is carried out by the genetic algorithm toolbox. The parametric model of the planar truss is established by the ANSYS Parametric Design Language. Solutions are obtained using the first-order method native. Genetic algorithms don’t always display better properties than others. Finally, a joint optimization method is proposed, which combines MATLAB genetic algorithm toolbox and the numerical algorithm based on the quasi-Newton method. The method is identified through the numerical example of the two-bar truss. The results indicate the joint optimization method can always converge to the global optimal solution.

You might also be interested in these eBooks

Civil, Structural and Environmental Engineering III

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 1065-1069)

Pages:

3442-3445

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1065-1069.3442

Citation:

Cite this paper

Online since:

December 2014

Authors:

Yan Hua Guo, Fei Fei Liu*, Ning Zhang, Tao Wang

Keywords:

ANSYS, Genetic Algorithm (GA), MATLAB, Numerical Algorithm, Optimization

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Guangzhou Chen, Huaming Xie, Xiangyou Lu. Nonlinear Optimization Based on Genetic Algorithm Toolbox of Matlab [J]. Computer Technology and Development, 2008, 18(3): 246-248. (In Chinese).

Google Scholar

[2] Qiulian Chen, Chengdong Wang. Optimization of Genetic Algorithm Toolbox Based on Matlab [J]. Modern Electronics Technique, 2007, 2: 124-127. (In Chinese).

Google Scholar

[3] Shuquan Luo. Comparision Between Readitional Optimized Algorithm and Heredity Algotithm [J]. Journal of Hubei University of Technology, 2007, 22(3): 32-35. (In Chinese).

Google Scholar

[4] Minqiang Li, Jisong Kou etc. The Basic Theory and Application of Genetic Algorithms [M]. Beijing: Science Press, 2004. (In Chinese).

Google Scholar

[5] Zhongzhen Zhang. Quadratic Program: Algorithm of Nonlinear Programing and Investment Combination [M]. Wuhan: Wuhan University Press, 2006. (In Chinese).

Google Scholar

[6] Nicholas Ali, Kamran Behdinan, Zouheir Fawaz. Applicability and Viability of a GA based Finite Element Analysis Architecture for Structural Design Optimization [J]. Computers and Structures, 2003, 81: 2259-2271.

DOI: 10.1016/s0045-7949(03)00255-4

Google Scholar