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Article type: Research Article
Authors: Finotto, Vitor C. | da Silva, Wilson R.L. | Štemberk, Petr | Valášek, Michael
Affiliations: Department of Robotics, Faculty of Mechanical Engineering, Czech Technical University in Prague, Prague, Czech Republic | Department of Concrete and Masonry Structures, Faculty of Civil Engineering, Czech Technical University in Prague, Prague, Czech Republic
Note: [] Corresponding author. Vitor C. Finotto, Department of Robotics, Faculty of Mechanical Engineering, Czech Technical University in Prague, Technicka 4, 166 29 Prague 6, Czech Republic. Tel.: +420 224 352 105; Fax: +420 224 916 709; E-mail: VitorCores.Finotto@fs.cvut.cz
Abstract: This paper sheds light on a fuzzy-genetic system applied to optimize cabled-truss structures. The optimization procedure combines ground structure approach, nonlinear finite element analysis, genetic algorithm, and fuzzy logic. The latter is used to include expertise in the evolutionary search to classify and filter individuals with low survival possibility (sp). The classification is based on a scale that varies from 0% to 100%, and the filtering depends on a threshold value (Spt) defined by the user. Particularly, the individuals with sp ≤ Spt are not evaluated, thereby decreasing the total number of evaluations. Although this approach proved suitable to reduce computational cost, the effect of different Spt values on the system's performance was not yet investigated. In that light, this work aims to present a sensitivity analysis of the fuzzy-genetic optimization system to variations of Spt. For that, the system was applied to ground structures with 10 elements and Spt values ranging from 0% to 100%. The results were compared by means of the analysis of variance test in order to investigate the effects of Spt on the system's performance and to identify the optimum Spt, which was found to be of 60% for the studied case.
Keywords: Analysis of variance, cabled-trusses, expert system, fuzzy logic, fuzzy-genetic, genetic algorithm, optimization
DOI: 10.3233/IFS-130871
Journal: Journal of Intelligent & Fuzzy Systems, vol. 26, no. 4, pp. 1931-1942, 2014
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