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Distributed Hybrid Genetic Algorithms for Structural Optimization  

우병헌 (롯데건설(주))
박효선 (연세대학교 건축·도시공학부)
Publication Information
Journal of the Computational Structural Engineering Institute of Korea / v.16, no.4, 2003 , pp. 407-417 More about this Journal
Abstract
Enen though several GA-based optimization algorithms have been successfully applied to complex optimization problems in various engineering fields, GA-based optimization methods are computationally too expensive for practical use in the field of structural optimization, particularly for large- scale problems. Furthermore, a successful implementation of GA-based optimization algorithm requires a cumbersome and trial-and-error routine related to setting of parameters dependent on a optimization problem. Therefore, to overcome these disadvantages, a high-performance GA is developed in the form of distributed hybrid genetic algorithm for structural optimization on a cluster of personal computers. The distributed hybrid genetic algorithm proposed in this paper consist of a simple GA running on a master computer and multiple μ-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 computational time required for structural optimization process can be drastically reduced and the dependency on the parameters can be avoided.
Keywords
structural optimization; genetic algorithms; high-performance computing; distributed computing; steel structures;
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