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Journal of Algorithms and Computation
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Volume Volume 41 (2007)
Ghasemi, M., Azadi, A. (2013). بهینه سازی قابهای فولادی با استفاده از الگوریتم وراثتی اصلاح شده هوشمند. Journal of Algorithms and Computation, 42(4), 465-476.
Mohammad Reza Ghasemi; Akbar Azadi. "بهینه سازی قابهای فولادی با استفاده از الگوریتم وراثتی اصلاح شده هوشمند". Journal of Algorithms and Computation, 42, 4, 2013, 465-476.
Ghasemi, M., Azadi, A. (2013). 'بهینه سازی قابهای فولادی با استفاده از الگوریتم وراثتی اصلاح شده هوشمند', Journal of Algorithms and Computation, 42(4), pp. 465-476.
Ghasemi, M., Azadi, A. بهینه سازی قابهای فولادی با استفاده از الگوریتم وراثتی اصلاح شده هوشمند. Journal of Algorithms and Computation, 2013; 42(4): 465-476.

بهینه سازی قابهای فولادی با استفاده از الگوریتم وراثتی اصلاح شده هوشمند

Article 3, Volume 42, Issue 4, August 2008, Page 465-476  XML PDF (769.87 K)
Document Type: Research Paper
Authors
Mohammad Reza Ghasemi* ; Akbar Azadi
Abstract
One of the major purposes of optimization in civil engineering is to perform a suitable design for the structure. This goal has to fulfill technical criteria and contain the minimum economical costs. Building frames are of the most customary civil engineering structures. Therefore, optimization of these types of structures could be of a great concern from the economical viewpoints. One of the current obstacles in such optimization problems is the local convergence debility. Thus, using means of tackling this problem seems necessary. Genetic Algorithm which is one of the optimization methods inspired by nature, has overcome this problem. In order to solve such problems, genetic algorithm needs a multiple analyses of structures. Therefore, in this study attempts were made to introduce and embed new formulae into a newly developed program to handle new techniques for selection and mutation as genetic operations. As for the aspects of application, the introduced techniques were inspected and investigated in the optimization of some planar and special braced steel frames. The outcome through comparisons proclaimed a considerable decrease in numbers of analyses as well as significant increases in the speed of convergence.
Keywords
Optimization; Spectral Response; Modified Genetic Algorithms; Selection; Mutation; Selective Oriented Method
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