Yazar "Lamberti, Luciano" seçeneğine göre listele

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    DISCRETE AND CONTINUOUS DESIGN OPTIMIZATION OF TOWER STRUCTURES USING THE JAYA ALGORITHM
    (E-Journal of New World Sciences Academy, 2018) Değertekin, S. Özgür; Lamberti, Luciano; Ugur, İ. Behram
    The Jaya algorithm (JA) which is very recently developed metaheuristic method is proposed for design optimization of tower structures. The distinctive characteristic of JA is that it does not use algorithm-specific parameters. The algorithm has a very simple formulation where the basic idea is to approach the best solution and escape from the worst solution. Continuous design optimization of 72-bar spatial tower and discrete design optimization of 244-bar spatial tower structure are used to demonstrate the validity of JA. The results show that the JA can obtain better designs than those of the other metaheuristic optimization methods in terms of optimized weight, standard deviation and number of structural analyses. 
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    Large-scale truss-sizing optimization with enhanced hybrid HS algorithm
    (MDPI, 2021) Değertekin, Sadık Özgür; Minooei, Mohammad; Santoro, Lorenzo; Trentadue, Bartolomeo; Lamberti, Luciano
    Abstract: Metaheuristic algorithms currently represent the standard approach to engineering optimization. A very challenging field is large-scale structural optimization, entailing hundreds of design variables and thousands of nonlinear constraints on element stresses and nodal displacements. However, very few studies documented the use of metaheuristic algorithms in large-scale structural optimization. In order to fill this gap, an enhanced hybrid harmony search (HS) algorithm for weight minimization of large-scale truss structures is presented in this study. The new algorithm, Large-Scale Structural Optimization–Hybrid Harmony Search JAYA (LSSO-HHSJA), developed here, combines a well-established method like HS with a very recent method like JAYA, which has the simplest and inherently most powerful search engine amongst metaheuristic optimizers. All stages of LSSO-HHSJA are aimed at reducing the number of structural analyses required in large-scale structural optimization. The basic idea is to move along descent directions to generate new trial designs, directly through the use of gradient information in the HS phase, indirectly by correcting trial designs with JA-based operators that push search towards the best design currently stored in the population or the best design included in a local neighborhood of the currently analyzed trial design. The proposed algorithm is tested in three large-scale weight minimization problems of truss structures. Optimization results obtained for the three benchmark examples, with up to 280 sizing variables and 37,374 nonlinear constraints, prove the efficiency of the proposed LSSO-HHSJA algorithm, which is very competitive with other HS and JAYA variants as well as with commercial gradient-based optimizers.
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    Mechanical identification of materials and structures with optical methods and metaheuristic optimization
    (MDPI AG, 2019) Ficarella, Elisa; Lamberti, Luciano; Deǧertekin, Sadık Özgür
    This study presents a hybrid framework for mechanical identification of materials and structures. The inverse problem is solved by combining experimental measurements performed by optical methods and non-linear optimization using metaheuristic algorithms. In particular, we develop three advanced formulations of Simulated Annealing (SA), Harmony Search (HS) and Big Bang-Big Crunch (BBBC) including enhanced approximate line search and computationally cheap gradient evaluation strategies. The rationale behind the new algorithms-denoted as Hybrid Fast Simulated Annealing (HFSA), Hybrid Fast Harmony Search (HFHS) and Hybrid Fast Big Bang-Big Crunch (HFBBBC)-is to generate high quality trial designs lying on a properly selected set of descent directions. Besides hybridizing SA/HS/BBBC metaheuristic search engines with gradient information and approximate line search, HS and BBBC are also hybridized with an enhanced 1-D probabilistic search derived from SA. The results obtained in three inverse problems regarding composite and transversely isotropic hyperelastic materials/structures with up to 17 unknown properties clearly demonstrate the validity of the proposed approach, which allows to significantly reduce the number of structural analyses with respect to previous SA/HS/BBBC formulations and improves robustness of metaheuristic search engines.