Distributed Hybrid Genetic Algorithms for Structural Optimization on a PC Cluster
Publication: Journal of Structural Engineering
Volume 132, Issue 12
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 -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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Acknowledgments
This material is based on work supported by the Ministry of Construction and Transportation of Korea (Grant No. UNSPECIFIEDC103A1040001-03A0204-00210) and research fund of the National Research Laboratory Program (Project No. KMST2005-01504) from the Ministry of Science and Technology.
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© 2006 ASCE.
History
Received: Feb 3, 2005
Accepted: Apr 19, 2006
Published online: Dec 1, 2006
Published in print: Dec 2006
Notes
Note. Associate Editor: Sherif El-Tawil
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