Abstract
A new hybrid genetic algorithm is presented for the solution of mixed-discrete nonlinear design optimization. In this approach, the genetic algorithm (GA) is used mainly to determine the optimal feasible region that contains the global optimum point, and the hybrid negative subgradient method integrated with discrete one-dimensional search is subsequently used to replace the GA to find the final optimum solution. The hybrid genetic algorithm, combining the advantages of random search and deterministic search methods, can improve the convergence speed and computational efficiency compared with some other GAs or random search methods. Several practical examples of mechanical design are tested using the computer program developed. The numerical results demonstrate the effectiveness and robustness of the proposed approach.
| Original language | English |
|---|---|
| Pages (from-to) | 1100-1112 |
| Number of pages | 13 |
| Journal | Journal of Mechanical Design, Transactions Of the ASME |
| Volume | 127 |
| Issue number | 6 |
| DOIs | |
| State | Published - Nov 1 2005 |
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ASJC Scopus subject areas
- Mechanical Engineering
Cite this
A hybrid genetic algorithm for mixed-discrete design optimization. / Rao, Singiresu S; Xiong, Ying.
In: Journal of Mechanical Design, Transactions Of the ASME, Vol. 127, No. 6, 01.11.2005, p. 1100-1112.Research output: Contribution to journal › Article
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TY - JOUR
T1 - A hybrid genetic algorithm for mixed-discrete design optimization
AU - Rao, Singiresu S
AU - Xiong, Ying
PY - 2005/11/1
Y1 - 2005/11/1
N2 - A new hybrid genetic algorithm is presented for the solution of mixed-discrete nonlinear design optimization. In this approach, the genetic algorithm (GA) is used mainly to determine the optimal feasible region that contains the global optimum point, and the hybrid negative subgradient method integrated with discrete one-dimensional search is subsequently used to replace the GA to find the final optimum solution. The hybrid genetic algorithm, combining the advantages of random search and deterministic search methods, can improve the convergence speed and computational efficiency compared with some other GAs or random search methods. Several practical examples of mechanical design are tested using the computer program developed. The numerical results demonstrate the effectiveness and robustness of the proposed approach.
AB - A new hybrid genetic algorithm is presented for the solution of mixed-discrete nonlinear design optimization. In this approach, the genetic algorithm (GA) is used mainly to determine the optimal feasible region that contains the global optimum point, and the hybrid negative subgradient method integrated with discrete one-dimensional search is subsequently used to replace the GA to find the final optimum solution. The hybrid genetic algorithm, combining the advantages of random search and deterministic search methods, can improve the convergence speed and computational efficiency compared with some other GAs or random search methods. Several practical examples of mechanical design are tested using the computer program developed. The numerical results demonstrate the effectiveness and robustness of the proposed approach.
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UR - http://www.scopus.com/inward/citedby.url?scp=28244439808&partnerID=8YFLogxK
U2 - 10.1115/1.1876436
DO - 10.1115/1.1876436
M3 - Article
AN - SCOPUS:28244439808
VL - 127
SP - 1100
EP - 1112
JO - Journal of Mechanical Design - Transactions of the ASME
JF - Journal of Mechanical Design - Transactions of the ASME
SN - 1050-0472
IS - 6
ER -