Sizing optimization of skeletal structures using teaching-learning based optimization

Toğan, Vedat; Mortazavi, Ali

doi:10.11121/ijocta.01.2017.00309

Cited by 7 publications

(5 citation statements)

References 33 publications

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“…The studies by Togan and Mortazavi [20] compared the use of the three possibilities for the value assumed by 𝑇 𝐹 , i.e., 1, 2 or a randomly generated number (equation 29), concluding that any of the three choices do not interfere in the final optimization result. For this work, the value 2 was used, the same used by Camp and Farshchin [4].…”

Section: Teacher Phasementioning

confidence: 99%

Teaching-Learning Based Optimization Method Considering Buckling and Slenderness Restriction for Space Trusses

2022

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The structural performance of a building is a function of several parameters and constraints whose association may offer non unique solutions which, however, meet the design requirements. Therefore, an optimization routine is needed to determine the best solution within the set of available alternatives. In this study, the TLBO method was implemented for weight-based optimization of space trusses. The algorithm applies restrictions related to the critical buckling load as well as the slenderness ratio, which are the basis to obtain reliable and realistic results. To assess the capability of the TLBO method, two reference cases and a transmission tower are subjected to the optimization analysis. In the transmission tower analysis, however, a more realistic approach is adopted as it also considers, through a safety factor, the plastic behavior in the critical buckling load constraint. With no optimization, the ideal weight increases by 101.36% when the critical buckling load is considered in the first two cases, which is consistent with the expected behavior. If the slenderness of the elements is also restricted, the ideal weight now rises by 300.78% from the original case and by 99.04% from the case where only the critical buckling load restriction is applied. Now, considering the critical buckling load and slenderness restriction with the TLBO method applied, a 18.28% reduction in the ideal weight is verified. In fact, the proposed optimization procedure converged to a better solution than that of the reference study, which is based on the genetic algorithms method.

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Section: Teacher Phasementioning

confidence: 99%

Teaching-Learning Based Optimization Method Considering Buckling and Slenderness Restriction for Space Trusses

2022

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“…It could be split into two basic parts: Teacher phase and Learner phase. TLBO has been used successfully for many multi-objective problems [19,20]. Algorithm 2 describes TLBO process.…”

Section: The Tlbo Algorithmmentioning

confidence: 99%

TLBO-Based Routing Approach for Wireless Sensor Networks

Ghazi¹,

Ahiod²,

Abbad³

2018

IJIES

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Routing in Wireless Sensor Networks (WSN) differs from routing in classical networks, as in WSN links are unreliable. Indeed, there is no infrastructure and the sensors have limited energy resources and low processing capacity. Routing problems in many cases are considered NP-hard problems. In these cases, metaheuristics can be the best solution for this kind of problems. Thus, many metaheuristics are used for routing in WSNs. Ant colony optimization (ACO) is among the most used metaheuristics, as it showed an excellent performance for routing. Due to the adaptation difficulties related to the ACO parameters, in this paper we propose a new optimization approach for routing problem using the Teaching Learning Based Optimization (TLBO). TLBO is a recent nature inspired metaheuristic based on the influence of the teacher in the class and learners interactions. In this work, TLBO algorithm is used for routing as it was proposed for continuous optimization problems, despite the fact that the routing in WSNs is a discrete optimization problem. Our proposed approach is shown to be competitive with ACObased approaches in terms of energy consumption and WSN lifetime. The experiments performed in varied scenarios and the results found, illustrate the efficiency of the TLBO approach against the well-known ACO approaches: Energy Efficient Ant Based Routing (EEABR) Algorithm and the Improved Ant Colony Optimization Routing Protocol (IACO).

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“…FA method imitating the firefly's mating behavior to search the domain and it used in several research works [11,14]. TLBO is modeled the educational relation between students and teacher in the classroom, this method and its modified versions also is utilizes as the optimizer tool in many different works [3,5,13,23,27,28]. DSO method models the food search strategies of the insect with the same name to search the problem domain [4].…”

Section: Introductionmentioning

confidence: 99%

Comparative assessment of five metaheuristic methods on distinct problems

Mortazavi¹

2019

DÜMF Mühendislik Dergisi

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Metaheuristic algorithms belong to the non-gradient based optimization methods. Accomplished studies in this area reveal that each of these methods mostly has its own affirmative and inconvenient aspects. So that, one might provide a high level of exploration while the other can perform a great level of exploitation. Thus, selecting the proper and efficient algorithm for a problem can highly affect both the convergence rate and the accuracy level. There are several different metaheuristic algorithms have been announced in the technical literature in the last decade. Therefore, performing an objective comparative assessment over some of these methods can provide a fundamental and fair attitude for researchers either to select an algorithm which is more fitted with their target(s) or to develop even more efficient methods. So, the current investigation deals with evaluating and comparing of five different metaheuristic techniques emerged from ten years ago up to now. The selected methods can be sorted chronologically as Firefly Algorithm (FA), Teaching and Learning Based Algorithm (TLBO), Drosophila Food Search (DSO) method, Ions Motion Optimization (IMO) and Butterfly Optimization Algorithm (BOA). Different properties of these algorithms as convergence rate, diversity variation, complexity and accuracy level of the final solutions are compared on both constrained and non-constrained optimization problems include mathematical functions, mechanical and structural problems. The results show that the cited methods show different performance depending on the type of the optimization problem but overally BOA and TLBO outperform the other algorithms on non-constrained and constrained problems, respectively.

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Sizing optimization of skeletal structures using teaching-learning based optimization

Cited by 7 publications

References 33 publications

Teaching-Learning Based Optimization Method Considering Buckling and Slenderness Restriction for Space Trusses

Teaching-Learning Based Optimization Method Considering Buckling and Slenderness Restriction for Space Trusses

TLBO-Based Routing Approach for Wireless Sensor Networks

Comparative assessment of five metaheuristic methods on distinct problems

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