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S. O. Degertekin
Department of Civil Engineering, Dicle University, 21280 Diyarbakir, Turkey

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Civil Engineering Department, Dicle University, 21280, Diyarbakir, Turkey

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Journal article
Published: 06 April 2021 in Applied Sciences
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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.

ACS Style

Sadik Degertekin; Mohammad Minooei; Lorenzo Santoro; Bartolomeo Trentadue; Luciano Lamberti. Large-Scale Truss-Sizing Optimization with Enhanced Hybrid HS Algorithm. Applied Sciences 2021, 11, 3270 .

AMA Style

Sadik Degertekin, Mohammad Minooei, Lorenzo Santoro, Bartolomeo Trentadue, Luciano Lamberti. Large-Scale Truss-Sizing Optimization with Enhanced Hybrid HS Algorithm. Applied Sciences. 2021; 11 (7):3270.

Chicago/Turabian Style

Sadik Degertekin; Mohammad Minooei; Lorenzo Santoro; Bartolomeo Trentadue; Luciano Lamberti. 2021. "Large-Scale Truss-Sizing Optimization with Enhanced Hybrid HS Algorithm." Applied Sciences 11, no. 7: 3270.

Journal article
Published: 25 December 2020 in Computers & Structures
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In this study, the parameter free Jaya algorithm (PFJA) is developed for sizing and layout optimization of truss structures subject to natural frequency constraints. The distinctive feature of PFJA is that it uses neither algorithm-specific parameters nor common parameters in the search process. Besides using an elitist strategy where new structural analyses are performed only if strictly necessary, PFJA adaptively changes population size in the optimization process. The validity of proposed PFJA is demonstrated by solving eight classical truss weight minimization problems including up to 59 sizing and layout design variables. The results obtained by the PFJA are compared with those of standard JA, modified Jaya algorithm (MJA) and other state-of-art metaheuristic algorithms in terms of optimized weight, convergence speed and several statistical parameters. Optimization results prove the superiority of PFJA over standard JA, MJA and other metaheuristic optimizers available in the literature.

ACS Style

S.O. Degertekin; G. Yalcin Bayar; L. Lamberti. Parameter free Jaya algorithm for truss sizing-layout optimization under natural frequency constraints. Computers & Structures 2020, 245, 106461 .

AMA Style

S.O. Degertekin, G. Yalcin Bayar, L. Lamberti. Parameter free Jaya algorithm for truss sizing-layout optimization under natural frequency constraints. Computers & Structures. 2020; 245 ():106461.

Chicago/Turabian Style

S.O. Degertekin; G. Yalcin Bayar; L. Lamberti. 2020. "Parameter free Jaya algorithm for truss sizing-layout optimization under natural frequency constraints." Computers & Structures 245, no. : 106461.

Original article
Published: 05 March 2020 in Engineering with Computers
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The performance-based optimum seismic design of steel frames is one of the most complicated and computationally demanding structural optimization problems. Metaheuristic optimization methods have been successfully used for solving engineering design problems over the last three decades. A very recently developed metaheuristic method called school-based optimization (SBO) will be utilized in the performance-based optimum seismic design of steel frames for the first time in this study. The SBO actually is an improved/enhanced version of teaching–learning-based optimization (TLBO), which mimics the teaching and learning process in a class where learners interact with the teacher and between themselves. Ad hoc strategies are adopted in order to minimize the computational cost of SBO results. The objective of the optimization problem is to minimize the weight of steel frames under interstory drift and strength constraints. Three steel frames previously designed by different metaheuristic methods including particle swarm optimization, improved quantum particle swarm optimization, firefly and modified firefly algorithms, teaching–learning-based optimization, and JAYA algorithm are used as benchmark optimization examples to verify the efficiency and robustness of the present SBO algorithm. Optimization results are compared with those of other state-of-the-art metaheuristic algorithms in terms of minimum structural weight, convergence speed, and several statistical parameters. Remarkably, in all test problems, SBO finds lighter designs with less computational effort than the TLBO and other methods available in metaheuristic optimization literature.

ACS Style

S. O. Degertekin; H. Tutar; L. Lamberti. School-based optimization for performance-based optimum seismic design of steel frames. Engineering with Computers 2020, 1 -15.

AMA Style

S. O. Degertekin, H. Tutar, L. Lamberti. School-based optimization for performance-based optimum seismic design of steel frames. Engineering with Computers. 2020; ():1-15.

Chicago/Turabian Style

S. O. Degertekin; H. Tutar; L. Lamberti. 2020. "School-based optimization for performance-based optimum seismic design of steel frames." Engineering with Computers , no. : 1-15.

Journal article
Published: 02 July 2019 in Materials
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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.

ACS Style

Elisa Ficarella; Luciano Lamberti; Sadik Ozgur Degertekin. Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization. Materials 2019, 12, 2133 .

AMA Style

Elisa Ficarella, Luciano Lamberti, Sadik Ozgur Degertekin. Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization. Materials. 2019; 12 (13):2133.

Chicago/Turabian Style

Elisa Ficarella; Luciano Lamberti; Sadik Ozgur Degertekin. 2019. "Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization." Materials 12, no. 13: 2133.

Journal article
Published: 08 April 2019 in Applied Soft Computing
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Discrete optimization of truss structures is a hard computing problem with many local minima. Metaheuristic algorithms are naturally suited for discrete optimization problems as they do not require gradient information. A recently developed method called Jaya algorithm (JA) has proven itself very efficient in continuous engineering problems. Remarkably, JA has a very simple formulation and does not utilize algorithm-specific parameters. This study presents a novel JA formulation for discrete optimization of truss structures under stress and displacement constraints. The new algorithm, denoted as discrete advanced JA (DAJA), implements efficient search mechanisms for generating new trial designs including discrete sizing, layout and topology optimization variables. Besides the JA’s basic concept of moving towards the best design of the population and moving away from the worst design, DAJA tries to form a set of descent directions in the neighborhood of each candidate designs thus generating high quality trial designs that are very likely to improve current population. Results collected in seven benchmark problems clearly demonstrate the superiority of DAJA over other state-of-the-art metaheuristic algorithms and multi-stage continuous-discrete optimization formulations.

ACS Style

S.O. Degertekin; L. Lamberti; I.B. Ugur. Discrete sizing/layout/topology optimization of truss structures with an advanced Jaya algorithm. Applied Soft Computing 2019, 79, 363 -390.

AMA Style

S.O. Degertekin, L. Lamberti, I.B. Ugur. Discrete sizing/layout/topology optimization of truss structures with an advanced Jaya algorithm. Applied Soft Computing. 2019; 79 ():363-390.

Chicago/Turabian Style

S.O. Degertekin; L. Lamberti; I.B. Ugur. 2019. "Discrete sizing/layout/topology optimization of truss structures with an advanced Jaya algorithm." Applied Soft Computing 79, no. : 363-390.

Journal article
Published: 29 February 2012 in Computers & Structures
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Harmony search (HS) algorithm was conceptualized using an analogy with music improvisation process where music players improvise the pitches of their instruments to obtain better harmony. Although the efficiency of HS algorithm has been proved in different engineering optimization applications, it is known that HS algorithm is quite sensitive to the tuning parameters. Several variants of HS algorithm have been developed to decrease the parameter-dependency character of HS algorithm. In this study, two improved harmony search algorithms called efficient harmony search algorithm (EHS) and self adaptive harmony search algorithm (SAHS) are proposed for sizing optimization of truss structures. Four classical truss structure weight minimization problems are presented to demonstrate the robustness of the proposed algorithms. The results of the present algorithms are compared with those of HS algorithm and other meta-heuristic algorithms recently developed in literature.

ACS Style

S.O. Degertekin. Improved harmony search algorithms for sizing optimization of truss structures. Computers & Structures 2012, 92-93, 229 -241.

AMA Style

S.O. Degertekin. Improved harmony search algorithms for sizing optimization of truss structures. Computers & Structures. 2012; 92-93 ():229-241.

Chicago/Turabian Style

S.O. Degertekin. 2012. "Improved harmony search algorithms for sizing optimization of truss structures." Computers & Structures 92-93, no. : 229-241.

Journal article
Published: 31 October 2005 in Composite Structures
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This paper presents a method to find globally optimum designs for two-dimensional composite structures subject to given in-plane static loads for which the critical failure mode is buckling. The aim is to maximize the buckling load capacity of laminated composites. For this purpose an improved version of simulated annealing algorithm, which is direct simulated annealing (DSA), was utilized. Fiber orientation in each layer was taken as a design variable. A computer code was developed, and results were obtained for several load cases.

ACS Style

Ozgur Erdal; Fazil O. Sonmez. Optimum design of composite laminates for maximum buckling load capacity using simulated annealing. Composite Structures 2005, 71, 45 -52.

AMA Style

Ozgur Erdal, Fazil O. Sonmez. Optimum design of composite laminates for maximum buckling load capacity using simulated annealing. Composite Structures. 2005; 71 (1):45-52.

Chicago/Turabian Style

Ozgur Erdal; Fazil O. Sonmez. 2005. "Optimum design of composite laminates for maximum buckling load capacity using simulated annealing." Composite Structures 71, no. 1: 45-52.