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Erschienen in: Structural and Multidisciplinary Optimization 1/2016

13.01.2016 | RESEARCH PAPER

Multi-objective crashworthiness optimization of perforated square tubes using modified NSGAII and MOPSO

verfasst von: Abolfazl Khalkhali, Majid Mostafapour, Seyed Mohamad Tabatabaie, Behnam Ansari

Erschienen in: Structural and Multidisciplinary Optimization | Ausgabe 1/2016

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Abstract

In this paper, multi-objective optimization of perforated square tubes is performed considering absorbed energy, peak crushing force and weight of the tube as three conflicting objective functions. In the multi-objective optimization problem (MOP), absorbed energy and peak crushing force are defined by polynomial models extracted using the software GEvoM based on the train and test data obtained from the numerical simulation of quasi-static crushing of the perforated square tubes using ABAQUS. To verify the numerical procedure, 16 different experimental tests are performed and then the experimental and numerical results are compared together. The comparison shows reasonable similarities between the numerical and experimental results. The MOP is solved using modified Non-dominated Sorting Genetic Algorithm II (NSGAII) and Multi-objective Particle Swarm Optimization (MOPSO) and then the solutions are combined for non-dominated sorting to obtain the non-dominated individuals of 3-objective optimization. 105 optimum points are extracted from the multi-objective optimization process. Finally, Nearest to Ideal Point (NIP) method and Technique for Ordering Preferences by Similarity to Ideal Solution (TOPSIS) method are employed to find trade-off optimum design points out of all non-dominated individuals compromising all three objective functions together.

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Metadaten
Titel
Multi-objective crashworthiness optimization of perforated square tubes using modified NSGAII and MOPSO
verfasst von
Abolfazl Khalkhali
Majid Mostafapour
Seyed Mohamad Tabatabaie
Behnam Ansari
Publikationsdatum
13.01.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
Structural and Multidisciplinary Optimization / Ausgabe 1/2016
Print ISSN: 1615-147X
Elektronische ISSN: 1615-1488
DOI
https://doi.org/10.1007/s00158-015-1385-y

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