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RISS 인기검색어
- 검색키워드
- 저자 : Vedat Toğan (검색결과 6 건)
- 무료
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Time-Cost Trade-off Optimization of Construction Projects using Teaching Learning Based Optimization
Vedat Toğan,M. Azim Eirgash 대한토목학회 2019 KSCE Journal of Civil Engineering Vol.23 No.1
Accelerating the project schedule raises the total cost of the project and shall be efficient only up to a certain limit. A Time-Cost Trade-off Problem (TCTP) is utilized to detect the optimal set of time-cost alternatives to enhance the overall construction project benefit. In this study, to find a set of Pareto front solutions, a multi-objective optimization model which is based on the Teaching- Learning Based Optimization (TLBO) incorporated with the Modified Adaptive Weight Approach (MAWA), is proposed. Four examples of construction projects taken from the technical literature ranging from 7 to 63 activities are investigated to show the performance of the MAWA-TLBO. The results are compared with those obtained using previously proposed models considering the optimal or near optimal solutions. It was found that the MAWA-TLBO algorithm works effectively for the TCTP in construction engineering and management field.
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An integrated particle swarm optimizer for optimization of truss structures with discrete variables
Ali Mortazavi,Vedat Toğan,Ayhan Nuhoğlu 국제구조공학회 2017 Structural Engineering and Mechanics, An Int'l Jou Vol.61 No.3
This study presents a particle swarm optimization algorithm integrated with weighted particle concept and improved fly-back technique. The rationale behind this integration is to utilize the affirmative properties of these new terms to improve the search capability of the standard particle swarm optimizer. Improved fly-back technique introduced in this study can be a proper alternative for widely used penalty functions to handle existing constraints. This technique emphasizes the role of the weighted particle on escaping from trapping into local optimum(s) by utilizing a recursive procedure. On the other hand, it guaranties the feasibility of the final solution by rejecting infeasible solutions throughout the optimization process. Additionally, in contrast with penalty method, the improved fly-back technique does not contain any adjustable terms, thus it does not inflict any extra ad hoc parameters to the main optimizer algorithm. The improved fly-back approach, as independent unit, can easily be integrated with other optimizers to handle the constraints. Consequently, to evaluate the performance of the proposed method on solving the truss weight minimization problems with discrete variables, several benchmark examples taken from the technical literature are examined using the presented method. The results obtained are comparatively reported through proper graphs and tables. Based on the results acquired in this study, it can be stated that the proposed method (integrated particle swarm optimizer, iPSO) is competitive with other metaheuristic algorithms in solving this class of truss optimization problems.
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Design of pin jointed structures using teaching-learning based optimization
Vedat Toğan 국제구조공학회 2013 Structural Engineering and Mechanics, An Int'l Jou Vol.47 No.2
A procedure employing a Teaching-Learning Based Optimization (TLBO) method is developed to design discrete pin jointed structures. TLBO process consists of two parts: the first part represents learning from teacher and the second part illustrates learning by interaction among the learners. The effectiveness of the TLBO method is demonstrated on the four design optimization problems. The results are compared with those obtained using other various evolutionary optimization methods considering the best solution, average solution, and computational effort. Consequently, the TLBO algorithm works effectively and demonstrates remarkable performance for the optimization of engineering design applications.
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Optimizing of Discrete Time–Cost in Construction Projects Using New Adaptive Weight Formulations
Vedat Toğan,Neslihan Berberoğlu,Tayfun Dede 대한토목학회 2022 KSCE JOURNAL OF CIVIL ENGINEERING Vol.26 No.2
In this paper, four new adaptive weight formulations are developed to enhance the performance of traditional Modified Adaptive Weight Approach (MAWA), which is the simplest way for solving the multi-objective optimization of discrete time-cost trade-off problem (DTCTP). Several numerical experiments are conducted to validate the newly proposed formulations. In addition, three meta-heuristic-based optimization algorithms are utilized to examine the variations within the results of the test instances. The proposed model provides several ways to solve DTCTP, as it includes different formulations and optimization algorithms. So, this model performs well for the medium and large-scale time-cost optimization problems on contrary to the traditional MAWA.
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A teaching learning based optimization for truss structures with frequency constraints
Tayfun Dede,Vedat Toğan 국제구조공학회 2015 Structural Engineering and Mechanics, An Int'l Jou Vol.53 No.4
Natural frequencies of the structural systems should be far away from the excitation frequency in order to avoid or reduce the destructive effects of dynamic loads on structures. To accomplish this goal, a structural optimization on size and shape has been performed considering frequency constraints. Such anoptimization problem has highly nonlinear property. Thus, the quality of the solution is not independent of the optimization technique to be applied. This study presents the performance evaluation of the recently proposed meta-heuristic algorithm called Teaching Learning Based Optimization (TLBO) as an optimization engine in the weight optimization of the truss structures under frequency constraints. Some examples regarding the optimization of trusses on shape and size with frequency constraints are solved. Also, the results obtained are tabulated for comparison. The results demonstrated that the performance of the TLBO is satisfactory. Additionally, TLBO is better than other methods in some cases.
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A multi-objective decision making model based on TLBO for the time – cost trade-off problems
Mohammad A. Eirgash,Vedat Toğan,Tayfun Dede 국제구조공학회 2019 Structural Engineering and Mechanics, An Int'l Jou Vol.71 No.2
In a project schedule, it is possible to reduce the time required to complete a project by allocating extra resources forcritical activities. However, accelerating a project causes additional expense. This issue is addressed by finding optimal set of timecostalternatives and is known as the time-cost trade-off problem in the literature. The aim of this study is to identify the optimal setof time-cost alternatives using a multiobjective teaching-learning-based optimization (TLBO) algorithm integrated with the nondominatedsorting concept and is applied to successfully optimize the projects ranging from a small to medium large projects. Numerical simulations indicate that the utilized model searches and identifies optimal / near optimal trade-offs between project timeand cost in construction engineering and management. Therefore, it is concluded that the developed TLBO-based multiobjectiveapproach offers satisfactorily solutions for time–cost trade-off optimization problems.
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