Design optimization of real world steel space frames using artificial bee colony algorithm with Levy flight distribution

Aydoğdu, İbrahim; Akın, Alper; Saka, M.P.

doi:10.1016/j.advengsoft.2015.10.013

Cited by 120 publications

(55 citation statements)

References 23 publications

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“…5. Artificial bee colony algorithm is widely used to obtain the solutions of structural optimization problems [51][52][53][54][55][56][57]. It is concluded in these studies that artificial bee colony algorithm is robust and efficient technique that performs better than some other metaheuristic algorithms such as genetic algorithm, ant colony algorithm, particle swarm optimizer, big bang-big crunch and imperialist competitive algorithms.…”

Section: Fitness(x Pmentioning

confidence: 99%

Comparative Study on Recent Metaheuristic Algorithms in Design Optimization of Cold-Formed Steel Structures

Saka

Çarbaş

Aydoğdu

et al. 2015

Computational Methods in Applied Sciences

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Sustainable construction aims at reducing the environmental impact of buildings on human health and natural environment by efficiently using energy, resources and reducing waste and pollution. Building construction has the capacity to make a major contribution to a more sustainable future of our World because this industry is one of the largest contributors to global warming. The use of coldformed steel framing in construction industry provides sustainable construction which requires less material to carry the same load compare to other materials and reduces amount of waste mimum design algorithms are developed for cold-formed steel frames made of thin-walled sections using the recent metaheuristic techniques. The algorithms considered are firefly, cuckoo search, artificial bee colony with levy flight, biogeography-based optimization and teaching-learning-based optimization algorithms. The design algorithms select the cold-formed thin-walled C-sections listed in AISI-LRFD (American Iron and Steel Institution, Load and Resistance Factor Design) in such a way that the design constraints specified by the code are satisfied and the weight of the steel frame is the minimum. A real size cold-formed steel building is optimized by using each of these algorithms and their performance in attaining the optimum designs is compared.

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Section: Fitness(x Pmentioning

confidence: 99%

Comparative Study on Recent Metaheuristic Algorithms in Design Optimization of Cold-Formed Steel Structures

Saka

Çarbaş

Aydoğdu

et al. 2015

Computational Methods in Applied Sciences

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“…These studies have mostly compared their result with those produced by [28] in which a comprehensive study is extensively reported on solving a number of numerical optimisation benchmarks. Most recently, [4] and [5] have implemented bee-inspired algorithms for a number of functions including the above mentioned ones, but, considering dimensions under 100 while we consider dimensions of 150.…”

Section: Relevant Workmentioning

confidence: 99%

Diversifying Search in Bee Algorithms for Numerical Optimisation

Düğenci

Aydın

2018

Computational Collective Intelligence

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Swarm intelligence offers useful instruments for developing collective behaviours to solve complex, ill-structured and large-scale problems. Efficiency in collective behaviours depends on how to harmonise the individual contributions so that a complementary collective effort can be achieved to offer a useful solution. The harmonisation helps blend diversification and intensification suitably towards efficient collective behaviours. In this study, two renown honeybees-inspired algorithms were analysed with respect to the balance of diversification and intensification and a hybrid algorithm is proposed to improve the efficiency accordingly. The proposed hybrid algorithm was tested with solving well-known highly dimensional numerical optimisation (benchmark) problems. Consequently, the proposed hybrid algorithm has demonstrated outperforming the two original bee algorithms in solving hard numerical optimisation benchmarks.

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“…With the continuous development of science and technology, many global optimization problems constantly exist in all most of engineering and science fields, such as queuing system [46]and structural design [4] [16]. Unfortunately, many of these problems are often characterized as non-convex, discontinuous or non differentiable, thus it is difficult to deal with them with traditional optimization algorithms.…”

Section: Introductionmentioning

confidence: 99%

Enhanced artificial bee colony with novel search strategy and dynamic parameter

Chen

2019

COMSIS J

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There is only one guiding solution in the search equation of Gaussian bare-bones artificial bee colony algorithm (ABC-BB), which is easy to result in the problem of premature convergence and trapping into the local minimum. In order to enhance the capability of escaping from local minimum without loss of the exploitation ability of ABC-BB, a new triangle search strategy is proposed. The candidate solution is generated among the triangle area formed by current solution, global best solution and any randomly selected elite solution to avoid the premature convergence problem. Moreover, the probability of crossover is controlled dynamically according to the successful search experience, which can enable ABC-BB to adapt all kinds of optimization problems with different landscapes. The experimental results on a set of 23 benchmark functions and two classic real-world engineering optimization problems show that the proposed algorithm is significantly better than ABC-BB as well as several recently-developed state-of-the-art evolution algorithms.

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Design optimization of real world steel space frames using artificial bee colony algorithm with Levy flight distribution

Cited by 120 publications

References 23 publications

Comparative Study on Recent Metaheuristic Algorithms in Design Optimization of Cold-Formed Steel Structures

Comparative Study on Recent Metaheuristic Algorithms in Design Optimization of Cold-Formed Steel Structures

Diversifying Search in Bee Algorithms for Numerical Optimisation

Enhanced artificial bee colony with novel search strategy and dynamic parameter

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