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Öğe Heat Transfer Search Algorithm for Sizing Optimization of Truss Structures(Latin Amer J Solids Structures, 2017) Degertekin, S. O.; Lamberti, L.; Hayalioglu, M. S.Heat transfer search (HTS) is a novel metaheuristic optimization algorithm that simulates the laws of thermodynamics and heat transfer. In this study, the HTS algorithm is adapted to truss structure optimization. Sizing optimization searches for the minimum weight of a structure subject to stress and displacement constraints. Three truss structures often taken as benchmarks in the optimization literature are selected here in order to verify the efficiency and robustness of the HTS algorithm. Optimization results indicate that HTS can obtain better designs (i.e. lighter trusses) than most of the state-of-the-art metaheuristic optimizers. The convergence behaviour of HTS also is as good as the other algorithms.Öğe A hybrid tabu-simulated annealing heuristic algorithm for optimum design of steel frames(Techno-Press, 2008) Degertekin, S. O.; Hayalioglu, M. S.; Ulker, M.A hybrid tabu-simulated annealing algorithm is proposed for the optimum design of steel frames. The special character of the hybrid algorithm is that it exploits both tabu search and simulated annealing algorithms simultaneously to obtain near optimum. The objective of optimum design problem is to minimize the weight of steel frames under the actual design constraints of AISC-LRFD specification. The performance and reliability of the hybrid algorithm were compared with other algorithms such as tabu search, simulated annealing and genetic algorithm using benchmark examples. The comparisons showed that the hybrid algorithm results in lighter structures for the presented examples.Öğe Minimum-weight design of non-linear steel frames using combinatorial optimization algorithms(Techno-Press, 2007) Hayalioglu, M. S.; Degertekin, S. O.Two combinatorial optimization algorithms, tabu search and simulated annealing, are presented for the minimum-weight design of geometrically non-linear steel plane frames. The design algorithms obtain minimum weight frames by selecting suitable sections from a standard set of steel sections such as American Institute of Steel Construction (AISC) wide-flange (W) shapes. Stress constraints of AISC Load and Resistance Factor Design (LRFD) specification, maximum and interstorey drift constraints and size constraints for columns were imposed on frames. The stress constraints of AISC Allowable Stress Design (ASD) were also mounted in the two algorithms. The comparisons between AISC-LRFD and AISC-ASD specifications were also made while tabu search and simulated annealing were used separately. The algorithms were applied to the optimum design of three frame structures. The designs obtained using tabu search were compared to those where simulated annealing was considered. The comparisons showed that the tabu search algorithm yielded better designs with AISC-LRFD code specification.Öğe Optimal load and resistance factor design of geometrically nonlinear steel space frames via tabu search and genetic algorithm(Elsevier Sci Ltd, 2008) Degertekin, S. O.; Saka, M. P.; Hayalioglu, M. S.In this paper, algorithms are presented for the optimum design of geometrically nonlinear steel space frames using tabu search and genetic algorithm. Tabu search utilizes the features of short-term memory facility (tabu list) and aspiration criteria. Genetic algorithm employs reproduction, crossover and mutation operators. The design algorithms obtain minimum weight frames by selecting suitable sections from a standard set of steel sections such as American Institute of Steel Construction (AISC) wide-flange (W) shapes. Stress constraints of AISC Load and Resistance Factor Design (LRFD) specification, maximum drift (lateral displacement) and interstorey drift constraints, size constraints for columns were imposed on frames. The algorithms were applied to the optimum design of three space frame structures. The designs obtained using tabu search were compared to those where genetic algorithm was considered. The comparisons showed that the former algorithm resulted in lighter structures. (c) 2007 Elsevier Ltd. All rights reserved.Öğe Optimum design of geometrically non-linear steel frames with semi-rigid connections using a harmony search algorithm(Techno-Press, 2009) Degertekin, S. O.; Hayalioglu, M. S.; Gorgun, H.The harmony search method based optimum design algorithm is presented for geometrically non-linear semi-rigid steel frames. Harmony search method is recently developed metaheuristic algorithm which simulates the process of producing a Musical performance. The optimum design algorithm arms at obtaining minimum weight steel ftarnes by selecting from standard set ol'steel Sections Such ZIS E-uropeall wide flange beams (HE sections). Strength constraints of Turkish Building Code flor Steel Structures (TS648) specification and displacement constraints were used in the optimum design formulation. The optimum design algorithm takes into account both the geometric non-linearity of the frame members and the semi-rigid behaviour of the be,beam-to-column Connections. The Frye-Morris polynomial model is used to calculate the momentrotation relation of beam-to-column connections. The ronustness of harmony search algorithm, ill comparison with genetic algorithms, is verified with two benchmark examples. The comparisons revealed that tile harmony search algorithm yielded not Only weight steel frames but also required less computational effort for the presented examples.Öğe Sizing truss structures using teaching-learning-based optimization(Pergamon-Elsevier Science Ltd, 2013) Degertekin, S. O.; Hayalioglu, M. S.Meta-heuristic search methods have been extensively used for optimization of truss structures over the past two decades. In this study, a new meta-heuristic search method called 'teaching-learning-based optimization' (TLBO) is applied for optimization of truss structures. The method makes use of the analogy between the learning process of learners and searching for designs to optimization problems. The TLBO consists of two phases: teacher phase and learner phase. 'Teacher phase' means learning from the teacher and 'learner phase' means learning by the interaction between learners. The validity of the method is demonstrated by the four design examples. Results obtained for the design examples revealed that although the TLBO developed slightly heavier designs than the other meta-heuristic methods in a few cases, it obtained results as good as or better than the other meta-heuristic optimization methods in terms of both the optimum solutions and the convergence capability in most cases. (C) 2012 Elsevier Ltd. All rights reserved.Öğe Tabu search based optimum design of geometrically non-linear steel space frames(Techno-Press, 2007) Degertekin, S. O.; Hayalioglu, M. S.; Ulker, M.In this paper, two algorithms are presented for the optimum design of geometrically nonlinear steel space frames using tabu search. The first algorithm utilizes the features of short-term memory (tabu list) facility and aspiration criteria and the other has long-term memory (back-tracking) facility in addition to the aforementioned features. The design algorithms obtain minimum weight frames by selecting suitable sections from a standard set of steel sections such as American Institute of Steel Construction (AISC) wide-flange (W) shapes. Stress constraints of AISC Allowable stress design (ASD) specification, maximum drift (lateral displacement) and interstorey drift constraints were imposed on the frames. The algorithms were applied to the optimum design of three space frame structures. The designs obtained using the two algorithms were compared to each other. The comparisons showed that the second algorithm resulted in lighter frames.
