An Automated Structural Optimisation Methodology for Scissor Structures Using a Genetic Algorithm. (18th January 2017)

• Record Type: Journal Article
• Authors: Koumar, Aushim; Tysmans, Tine; Filomeno Coelho, Rajan; De Temmerman, Niels
• Other Names: Hajipour Vahid Academic Editor.
• Is Part Of: Applied computational intelligence and soft computing. Volume 2017(2017)
• Publication Date: 2017-01-18
• Subjects: Computational intelligence -- Periodicals; Soft computing -- Periodicals; 006.305
• DOI: 10.1155/2017/6843574
• Languages: English
• ISSNs: 1687-9724

Abstract

Abstract : We developed a fully automated multiobjective optimisation framework using genetic algorithms to generate a range of optimal barrel vault scissor structures. Compared to other optimisation methods, genetic algorithms are more robust and efficient when dealing with multiobjective optimisation problems and provide a better view of the search space while reducing the chance to be stuck in a local minimum. The novelty of this work is the application and validation (using metrics) of genetic algorithms for the shape and size optimisation of scissor structures, which has not been done so far for two objectives. We tested the feasibility and capacity of the methodology by optimising a 6 m span barrel vault to weight and compactness and by obtaining optimal solutions in an efficient way using NSGA-II. This paper presents the framework and the results of the case study. The in-depth analysis of the influence of the optimisation variables on the results yields new insights which can help in making choices with regard to the design variables, the constraints, and the number of individuals and generations in order to obtain efficiently a trade-off of optimal solutions.