دوره 8، شماره 4 - ( 7-1397 )                   جلد 8 شماره 4 صفحات 625-656 | برگشت به فهرست نسخه ها

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Darvishi P, Shojaee S. SIZE AND GEOMETRY OPTIMIZATION OF TRUSS STRUCTURES USING THE COMBINATION OF DNA COMPUTING ALGORITHM AND GENERALIZED CONVEX APPROXIMATION METHOD. International Journal of Optimization in Civil Engineering. 2018; 8 (4) :625-656
URL: http://ijoce.iust.ac.ir/article-1-366-fa.html
SIZE AND GEOMETRY OPTIMIZATION OF TRUSS STRUCTURES USING THE COMBINATION OF DNA COMPUTING ALGORITHM AND GENERALIZED CONVEX APPROXIMATION METHOD. دانشگاه علم و صنعت ایران. 1397; 8 (4) :625-656

URL: http://ijoce.iust.ac.ir/article-1-366-fa.html


چکیده:   (615 مشاهده)
In recent years, the optimization of truss structures has been considered due to their several applications and their simple structure and rapid analysis. DNA computing algorithm is a non-gradient-based method derived from numerical modeling of DNA-based computing performance by new computers with DNA memory known as molecular computers. DNA computing algorithm works based on collective intelligence. It works with doing random search in the search space and creating the initial random population by modeling DNA-based computing operators and applies the operators derived from genetic algorithm to achieve the optimum solution of the objective function. Generalized Convex Approximation (GCA) method is a gradient-based method that with approximation of the main function and starting from a point, finds the optimum solution using information about functions and their gradient. In this research, in order to minimize the weight of truss, the cross-section areas of the elements as discrete variables are optimized by DNA computing algorithm, and the coordinates of truss nodes as continuous variables are optimized by Generalized Convex Approximation (GCA) method. Therefore, to simultaneously optimize the size and geometry of truss structures, these two methods are used in combination. The results of numerical examples show the proper functioning of this process.
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نوع مطالعه: پژوهشي | موضوع مقاله: Optimal design
دریافت: ۱۳۹۶/۱۲/۸ | پذیرش: ۱۳۹۶/۱۲/۸ | انتشار: ۱۳۹۶/۱۲/۸

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