Upper Bound Strategy in Optimum Design of Truss Structures: A Big Bang-Big Crunch Algorithm Based Application

Azad, Saeid Kazemzadeh; Hasançebi, O.; Erol, Osman Kaan

doi:10.1260/1369-4332.16.6.1035

Cited by 18 publications

(27 citation statements)

References 22 publications

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“…The HFSA, HFHS and HFBBBC algorithms developed in this study for mechanical identification problems were tested on two composite structures and a hyperelastic biological membrane. They were compared with other SA/HS/BBBC variants (e.g., [77,81,83,84] and their successive enhancements [162,163,164,165,166]) including gradient information in the optimization search, as well as with adaptive harmony search [170,171], big bang-big crunch with upper bound strategy (BBBC-UBS) [172], JAYA [35], MATLAB Sequential Quadratic Programming (MATLAB-SQP) [173] and ANSYS built-in optimization routines [174]. The ANSYS built-in optimization routines (e.g., gradient-based, zero order and response surface approximation) were run in cascade or alternated in order to maximize their efficiency.…”

Section: Test Problems and Resultsmentioning

confidence: 99%

“…The abovementioned comparison should be considered very indicative for the following reasons: Adaptive HS [170,171] and BBBC-UBS [172] represent state-of-the-art formulations of harmony search and big bang-big crunch, which have been successfully utilized in many optimization problems. In particular, the adaptive HS algorithm adaptively changes internal parameters without any intervention by the user: this approach is very similar to what is done by HFHS.…”

Section: Test Problems and Resultsmentioning

confidence: 99%

“…In particular, the adaptive HS algorithm adaptively changes internal parameters without any intervention by the user: this approach is very similar to what is done by HFHS. The BBBC-UBS algorithm [172] immediately discharges trial designs that certainly would not improve the current best design included in the population, thus saving computational cost; this elitist strategy is somehow consistent with the rationale followed by HFSA, HFHS and HFBBBC that always try to generate trial designs lying on descent directions.JAYA [35] is one of the most recently developed metaheuristic algorithms that has soon emerged as a very powerful method and gathered great consideration from optimization experts. The basic idea of JAYA is very simple yet very effective: search process tries to move toward the best design and avoid the worst design of the population.…”

Section: Test Problems and Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization

2019

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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.

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Section: Test Problems and Resultsmentioning

confidence: 99%

Section: Test Problems and Resultsmentioning

confidence: 99%

Section: Test Problems and Resultsmentioning

confidence: 99%

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Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization

2019

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“…The total number of structural analysis required to reach the optimum design is large similar to most of metaheuristic algorithms. This number can be reduced by carrying out some enhancement in the algorithm, such as adding upper bound strategy (UBS) [21,42,43], as a future work. The idea behind UBS is to detect those candidate designs which have no chance to improve the search during the optimization process.…”

Section: Numerical Examplesmentioning

confidence: 99%

Optimum structural design of spatial steel frames via biogeography-based optimization

Çarbaş

2016

Neural Comput & Applic

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Metaheuristic algorithms have provided an efficient tool for designers by which discrete optimum design of real-size steel space frames under design code requirements can be obtained. In this study, the optimum sizing design of steel space frames is formulated according to provisions of Load and Resistance Factor Design-American Institute of Steel Construction. The weight of the steel frame is taken as objective function. The design algorithm selects the appropriate W sections for members of the steel frame such that the frame weight is the minimum and design code limitations are satisfied. The biogeographybased optimization algorithm is utilized to find out the optimum solution of the discrete programming problem. This algorithm is one of the recent additions to metaheuristic techniques which are based on theory of island biogeography where each habitat is assumed to be potential solution for the design problem. The performance of the biogeography-based optimization algorithm is compared with other recent metaheuristic algorithms such as adaptive firefly algorithm, teaching and learning-based optimization, artificial bee colony optimization, dynamic harmony search algorithm, and ant colony algorithm. It is shown that biogeography-based optimization algorithm outperforms other metaheuristic techniques in the design examples considered.

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“…Hasançebi and Azad (2013) presented a new redistribution equation for size optimization of skeletal structures. Azad et al (2013) proposed the Upper Bound Strategy (UBS) to reduce the number of analyses and validated the performance of their strategy with various trusssizing problems. Another hybrid algorithm is presented by Kaveh and Mahdavi (2013) for size optimization of trusses with multiple frequency constraints.…”

Section: Introductionmentioning

confidence: 99%

A Study on Size Optimization of Trusses with BB-BC Algorithm: Review and Numerical Experiments

Özbaşaran¹

2018

AKU-J. Sci. Eng.

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In the last decades, great attention has been paid to structural optimization with stochastic methods. The applications of metaheuristic algorithms have become popular, which mostly provide solutions with adequate precision for the structural optimization problems from an engineering point of view. However, these algorithms should be specifically tuned for the considered problem to obtain satisfactory results. Big Bang-Big Crunch is one of the efficient metaheuristic optimization algorithms that is based on the famous theory on the evolution of the universe. A considerable number of researchers presented applications of the Big Bang-Big Crunch algorithm for the size optimization of trusses, which is an inviting challenge in the structural optimization field. This study revisits the size optimization of trusses with continuous variables using the Big Bang-Big Crunch algorithm, discusses the previously introduced improvements and presents the results of a few experimental modifications.

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Upper Bound Strategy in Optimum Design of Truss Structures: A Big Bang-Big Crunch Algorithm Based Application

Cited by 18 publications

References 22 publications

Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization

Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization

Optimum structural design of spatial steel frames via biogeography-based optimization

A Study on Size Optimization of Trusses with BB-BC Algorithm: Review and Numerical Experiments

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