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Öğe Comparison of three novel hybrid metaheuristic algorithms for structural optimization problems(Pergamon-Elsevier Science Ltd, 2021) Ficarella, E.; Lamberti, L.; Degertekin, S. O.Computational efficiency of metaheuristic optimization algorithms depends on appropriate balance between exploration and exploitation. An important concern in metaheuristic optimization is that there is no guarantee that new trial designs will always improve the current best record. In this regard, there not exist any metaheuristic algorithm inherently superior over all other methods. This study compares three advanced formulations of state-of-the-art metaheuristic optimization algorithms - 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 formulations is to generate high quality trial designs lying on a properly chosen set of descent directions. This is done throughout the optimization process. Besides hybridizing the metaheuristic search engines of HS/BBBC/SA with gradient information and approximate line search, HS and BBBC are also hybridized with an enhanced 1-D probabilistic search derived from SA. All these enhancements allow to approach more quickly the region of design space hosting the global optimum. The new algorithms are tested in four weight minimization problems of skeletal structures and three mechanical/civil engineering design problems with up to 204 continuous/discrete variables and 20,070 nonlinear constraints. All test problems may contain multiple local minima. The optimization results and an extensive comparison with the literature clearly demonstrate the validity of the proposed approach which allows to significantly reduce the number of function evaluations/structural analyses with respect to the literature and improves robustness of metaheuristic search engines. (C) 2020 Elsevier Ltd. All rights reserved.Öğe Discrete sizing/layout/topology optimization of truss structures with an advanced Jaya algorithm(Elsevier Science Bv, 2019) Degertekin, S. O.; Lamberti, L.; Ugur, I. B.Discrete optimization of truss structures is a hard computing problem with many local minima. Metaheuristic algorithms are naturally suited for discrete optimization problems as they do not require gradient information. A recently developed method called Jaya algorithm (JA) has proven itself very efficient in continuous engineering problems. Remarkably, JA has a very simple formulation and does not utilize algorithm-specific parameters. This study presents a novel JA formulation for discrete optimization of truss structures under stress and displacement constraints. The new algorithm, denoted as discrete advanced JA (DAJA), implements efficient search mechanisms for generating new trial designs including discrete sizing, layout and topology optimization variables. Besides the JA's basic concept of moving towards the best design of the population and moving away from the worst design, DAJA tries to form a set of descent directions in the neighborhood of each candidate designs thus generating high quality trial designs that are very likely to improve current population. Results collected in seven benchmark problems clearly demonstrate the superiority of DAJA over other state-of-the-art metaheuristic algorithms and multi-stage continuous-discrete optimization formulations. (C) 2019 Elsevier B.V. All rights reserved.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 Parameter free Jaya algorithm for truss sizing-layout optimization under natural frequency constraints(Pergamon-Elsevier Science LTD, 2021) Değertekin, S.Ö.; Bayar, G.Y.; Lamberti, L.In this study, the parameter free Jaya algorithm (PFJA) is developed for sizing and layout optimization of truss structures subject to natural frequency constraints. The distinctive feature of PFJA is that it uses neither algorithm-specific parameters nor common parameters in the search process. Besides using an elitist strategy where new structural analyses are performed only if strictly necessary, PFJA adaptively changes population size in the optimization process. The validity of proposed PFJA is demonstrated by solving eight classical truss weight minimization problems including up to 59 sizing and layout design variables. The results obtained by the PFJA are compared with those of standard JA, modified Jaya algorithm (MJA) and other state-of-art metaheuristic algorithms in terms of optimized weight, convergence speed and several statistical parameters. Optimization results prove the superiority of PFJA over standard JA, MJA and other metaheuristic optimizers available in the literature. (C) 2020 Elsevier Ltd. All rights reserved.Öğe School-based optimization for performance-based optimum seismic design of steel frames(Springer, 2021) Degertekin, S. O.; Tutar, H.; Lamberti, L.The performance-based optimum seismic design of steel frames is one of the most complicated and computationally demanding structural optimization problems. Metaheuristic optimization methods have been successfully used for solving engineering design problems over the last three decades. A very recently developed metaheuristic method called school-based optimization (SBO) will be utilized in the performance-based optimum seismic design of steel frames for the first time in this study. The SBO actually is an improved/enhanced version of teaching-learning-based optimization (TLBO), which mimics the teaching and learning process in a class where learners interact with the teacher and between themselves. Ad hoc strategies are adopted in order to minimize the computational cost of SBO results. The objective of the optimization problem is to minimize the weight of steel frames under interstory drift and strength constraints. Three steel frames previously designed by different metaheuristic methods including particle swarm optimization, improved quantum particle swarm optimization, firefly and modified firefly algorithms, teaching-learning-based optimization, and JAYA algorithm are used as benchmark optimization examples to verify the efficiency and robustness of the present SBO algorithm. Optimization results are compared with those of other state-of-the-art metaheuristic algorithms in terms of minimum structural weight, convergence speed, and several statistical parameters. Remarkably, in all test problems, SBO finds lighter designs with less computational effort than the TLBO and other methods available in metaheuristic optimization literature.Öğe Sizing, layout and topology design optimization of truss structures using the Jaya algorithm(Elsevier, 2018) Degertekin, S. O.; Lamberti, L.; Ugur, I. B.A very recently developed metaheuristic method called Jaya algorithm (JA) is implemented in this study for sizing and layout optimization of truss structures. The main feature of JA is that it does not require setting algorithm-specific parameters. The algorithm has a very simple formulation where the basic idea is to approach the best solution and escape from the worst solution. The original JA formulation is modified in this research in order to improve convergence speed and reduce the number of structural analyses required in the optimization process. The suitability of JA for truss optimization is investigated by solving six classical weight minimization problems of truss structures including sizing, layout and large-scale optimization problems with up to 204 design variables. Discrete sizing/layout variables and simplified topology optimization also are considered. The test problems solved in this study are very common benchmarks in structural optimization and practically describe all scenarios that may be faced by designers. The results demonstrate that JA can obtain better designs than those of the other state-of-the-art metaheuristic and gradient-based optimization methods in terms of optimized weight, standard deviation and number of structural analyses. (C) 2017 Elsevier B.V. All rights reserved.
