A selective genetic algorithm for multiobjective optimization of cross sections in 3D trussed structures based on a spatial sensitivity analysis. Issue 2 (8th August 2016)

• Record Type: Journal Article
• Authors: Mroginski, Javier Luis; Beneyto, Pablo Alejandro; Gutierrez, Guillermo Alejandro; Di Rado, Ariel
• Editors: Feng Yue, Zhu; Xiao, Heng
• Is Part Of: Multidiscipline modeling in materials and structures. Volume 12:Issue 2(2016)
• Publication Date: 2016-08-08
• Subjects: Materials -- Mathematical models -- Periodicals; Engineering -- Mathematical models -- Periodicals; 620.11015118
• DOI: 10.1108/MMMS-08-2015-0048
• Languages: English
• ISSNs: 1573-6105

Abstract

Abstract : Purpose: There are many problems in civil or mechanical engineering related to structural design. In such a case the solution techniques which lead to deterministic results are no longer valid due to the heuristic nature of design problems. In this article a computational tool based on genetic algorithms, applied to the optimal design of cross sections (solid tubes) of 3D truss structures is proposed. Design/methodology/approach: The main feature of this genetic algorithm approach is the introduction of a selective-smart method developed in order to improve the convergence rate of large optimization problems. This selective genetic algorithm is based on a preliminary sensitivity analysis performed over each variable, in order to reduce the search space of the evolutionary process. In order to account for the optimization of the total weight, the displacement (of a specific section) and the internal stresses distribution of the structure a multiobjective optimization function was proposed. Findings: The numerical results presented in this article show a significant improvement in the convergence rate as well as an important reduction in the relative error, compared to the exact solution. Originality/value: The variables sensitivity analysis put forward in this approach introduces a significant improvement in the convergence rate of the genetic algorithm proposed in this article.