Abstract
Even though several genetic algorithm (GA)-based optimization algorithms have been successfully applied to complex optimization problems in various engineering fields, such methods are computationally too expensive for practical use in the field of structural optimization, particularly for large-scale problems. Furthermore, the successful implementation of GA-based optimization algorithm requires a cumbersome routine through trial-and-error for tuning the GA parameters that are different depending on each problem. Therefore, to overcome these difficulties, a high-performance GA is developed in the form of a distributed hybrid genetic algorithm for structural optimization, implemented on a cluster of personal computers. The distributed hybrid genetic algorithm proposed in this paper consists of a μ -GA running on a master computer and multiple simple GAs running on slave computers. The algorithm is implemented on a PC cluster and applied to the minimum weight design of steel structures. The results show that the computation time required for GA-based optimization can be drastically reduced and the problem-dependent parameter tuning process can be avoided.
| Original language | English |
|---|---|
| Article number | 013612QST |
| Pages (from-to) | 1890-1897 |
| Number of pages | 8 |
| Journal | Journal of Structural Engineering |
| Volume | 132 |
| Issue number | 12 |
| DOIs | |
| Publication status | Published - 2006 Nov 28 |
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All Science Journal Classification (ASJC) codes
- Civil and Structural Engineering
- Building and Construction
- Materials Science(all)
- Mechanics of Materials
- Mechanical Engineering
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Distributed hybrid genetic algorithms for structural optimization on a PC cluster. / Park, Hyo Seon; Kwon, Yun Han; Seo, Ji Hyun; Woo, Byung Hun.
In: Journal of Structural Engineering, Vol. 132, No. 12, 013612QST, 28.11.2006, p. 1890-1897.Research output: Contribution to journal › Article
TY - JOUR
T1 - Distributed hybrid genetic algorithms for structural optimization on a PC cluster
AU - Park, Hyo Seon
AU - Kwon, Yun Han
AU - Seo, Ji Hyun
AU - Woo, Byung Hun
PY - 2006/11/28
Y1 - 2006/11/28
N2 - Even though several genetic algorithm (GA)-based optimization algorithms have been successfully applied to complex optimization problems in various engineering fields, such methods are computationally too expensive for practical use in the field of structural optimization, particularly for large-scale problems. Furthermore, the successful implementation of GA-based optimization algorithm requires a cumbersome routine through trial-and-error for tuning the GA parameters that are different depending on each problem. Therefore, to overcome these difficulties, a high-performance GA is developed in the form of a distributed hybrid genetic algorithm for structural optimization, implemented on a cluster of personal computers. The distributed hybrid genetic algorithm proposed in this paper consists of a μ -GA running on a master computer and multiple simple GAs running on slave computers. The algorithm is implemented on a PC cluster and applied to the minimum weight design of steel structures. The results show that the computation time required for GA-based optimization can be drastically reduced and the problem-dependent parameter tuning process can be avoided.
AB - Even though several genetic algorithm (GA)-based optimization algorithms have been successfully applied to complex optimization problems in various engineering fields, such methods are computationally too expensive for practical use in the field of structural optimization, particularly for large-scale problems. Furthermore, the successful implementation of GA-based optimization algorithm requires a cumbersome routine through trial-and-error for tuning the GA parameters that are different depending on each problem. Therefore, to overcome these difficulties, a high-performance GA is developed in the form of a distributed hybrid genetic algorithm for structural optimization, implemented on a cluster of personal computers. The distributed hybrid genetic algorithm proposed in this paper consists of a μ -GA running on a master computer and multiple simple GAs running on slave computers. The algorithm is implemented on a PC cluster and applied to the minimum weight design of steel structures. The results show that the computation time required for GA-based optimization can be drastically reduced and the problem-dependent parameter tuning process can be avoided.
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U2 - 10.1061/(ASCE)0733-9445(2006)132:12(1890)
DO - 10.1061/(ASCE)0733-9445(2006)132:12(1890)
M3 - Article
AN - SCOPUS:33751294074
VL - 132
SP - 1890
EP - 1897
JO - Journal of Structural Engineering
JF - Journal of Structural Engineering
SN - 0733-9445
IS - 12
M1 - 013612QST
ER -