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Article Citation - WoS: 11Citation - Scopus: 12E-Constraint Guided Stochastic Search With Successive Seeding for Multi-Objective Optimization of Large-Scale Steel Double-Layer Grids(Elsevier, 2022) Azad, Saeid Kazemzadeh; Aminbakhsh, SamanThis paper proposes a design-driven structural optimization algorithm named e-constraint guided stochastic search (e-GSS) for multi-objective design optimization of large-scale steel double-layer grids having numerous discrete design variables. Based on the well-known e-constraint method, first, the multi-objective optimization problem is transformed into a set of single-objective optimization problems. Next, each single-objective optimization problem is tackled using an enhanced reformulation of the standard guided stochastic search algorithm proposed based on a stochastic maximum incremental/decremental step size approach. Moreover, a successive seeding strategy is employed in conjunction with the proposed e-GSS algorithm to improve its performance in multi-objective optimization of large-scale steel double-layer grids. The numerical results obtained through multi-objective optimization of three challenging test examples, namely a 1728-member double-layer compound barrel vault, a 2304-member double-layer scallop dome, and a 2400-member double-layer multi-radial dome, demonstrate the usefulness of the proposed e-GSS algorithm in generating Pareto fronts of the foregoing multi-objective structural optimization problems with up to 2400 distinct sizing variables.Article Citation - WoS: 25Citation - Scopus: 32High-Dimensional Optimization of Large-Scale Steel Truss Structures Using Guided Stochastic Search(Elsevier Science inc, 2021) Azad, Saeid Kazemzadeh; Aminbakhsh, SamanDespite a plethora of truss optimization algorithms devised in the recent literature of structural optimization, still high-dimensional large-scale truss optimization problems have not been properly tackled basically due to the excessive computational effort required to handle the foregoing instances. In this study, application of a recently developed design-driven heuristic, namely guided stochastic search (GSS), is extended to a more challenging class of truss optimization problems having thousands of design variables. Two variants of the algorithm, namely GSSA and GSSB, have been employed for sizing optimization of four high-dimensional examples of steel trusses, i.e., a 2075-member single-layer onion dome, a 2688-member double-layer open dome, a 6000-member doublelayer scallop dome, and a 15048-member double-layer grid as per AISC-LRFD specification. The numerical results obtained indicate the efficiency of GSSA and GSSB in handling high-dimensional instances of large-scale steel trusses with up to 15048 discrete design variables.Article Citation - WoS: 11Citation - Scopus: 11A Standard Benchmarking Suite for Structural Optimization Algorithms: Iscso 2016-2022(Elsevier Science inc, 2023) Azad, Saeid Kazemzadeh; Azad, Saeıd Kazemzadeh; Azad, Sina Kazemzadeh; Azad, Saeıd Kazemzadeh; Department of Civil Engineering; Department of Civil EngineeringBenchmarking is an essential part of developing efficient structural optimization techniques. Despite the advent of numerous metaheuristic techniques for solving truss optimization problems, benchmarking new algorithms is often carried out using a selection of classic test examples which are indeed unchallenging for contemporary sophisticated optimization algorithms. Furthermore, the limited optimization results available in the literature on new test examples are usually not accurately comparable. This is typically due to the lack of infromation about the performance of the investigated algorithms and the inconsistencies between the studies in terms of adopted test examples for benchmarking, optimization problem formulation, maximum number of objective function evaluations and other similar issues. Accordingly, there exists a need for developing new standard test suites composed of easily reproducible challenging test examples with rigorous and comparable performance evaluation results of algorithms on these test suites. To this end, the present work aims to propose a new baseline for benchmarking structural optimization algorithms, using a set of challenging sizing and shape optimization problems of truss structures selected from the international student competition in structural optimization (ISCSO) instances. The most recent six structural optimization examples from the ISCSO are tackled using a representative metaheuristic structural optimization algorithm. The statistical results of all the optimization runs using the proposed benchmarking suite are provided to pave the way for more rigorous benchmarking of structural optimization algorithms.Article Citation - WoS: 33Citation - Scopus: 36Monitored Convergence Curve: a New Framework for Metaheuristic Structural Optimization Algorithms(Springer, 2019) Azad, Saeid KazemzadehMetaheuristic optimization algorithms, by nature, depend on random processes, and therefore, performing numerous algorithm runs is inevitable to locate a reasonably good solution. Although executing the algorithms for small-size or trivial structural optimization problems could be computationally affordable, when dealing with challenging optimization problems, there is almost no chance of performing numerous independent runs of metaheuristics in a timely manner. This difficulty is basically due to the limitations in computational technologies as well as the excessive computational cost of such problems. In such cases that the number of independent runs is limited to a small number, each optimization run becomes highly valuable and, therefore, the stability of results becomes much more significant. In the present study, it is attempted to monitor the convergence curve of each succeeding run of the algorithm with respect to the information obtained in the previous runs. An easy-to-implement yet efficient framework is proposed for metaheuristic structural optimization algorithms where every succeeding run is monitored at certain intervals named as solution monitoring period. The solution monitoring period is selected such that, at each run, on the one hand, the algorithm could explore the search space to improve the solution quality, and on the other hand, the algorithm is occasionally forced to return to the previously visited more promising solutions if it is not able to improve the solution after a certain number of iterations. The numerical experiments using challenging test instances with up to 354 design variables indicate that, in general, the proposed approach helps to improve the solution quality as well as the robustness or stability of results in metaheuristic structural optimization.Article Citation - WoS: 23Citation - Scopus: 21Design Optimization of Real-Size Steel Frames Using Monitored Convergence Curve(Springer, 2021) Azad, Saeid Kazemzadeh; Azad, Saeıd Kazemzadeh; Azad, Saeıd Kazemzadeh; Department of Civil Engineering; Department of Civil EngineeringIt is an undeniable fact that there are main challenges in the use of metaheuristics for optimal design of real-size steel frames in practice. In general, steel frame optimization problems usually require an inordinate amount of processing time where the main portion of computational effort is devoted to myriad structural response computations during the optimization iterations. Moreover, the inherent complexity of steel frame optimization problems may result in poor performance of even contemporary or advanced metaheuristics. Beside the challenging nature of such problems, significant difference in geometrical properties of two adjacent steel sections in a list of available profiles can also mislead the optimization algorithm and may result in trapping the algorithm in a poor local optimum. Consequently, akin to other challenging engineering optimization instances, significant fluctuations could be observed in the final results of steel frame optimization problems over multiple runs even using contemporary metaheuristics. Accordingly, the main focus of this study is to improve the solution quality as well as the stability of results in metaheuristic optimization of real-size steel frames using a recently developed framework so-called monitored convergence curve (MCC). Two enhanced variants of the well-known big bang-big crunch algorithm are adopted as typical contemporary metaheuristic algorithms to evaluate the usefulness of the MCC framework in steel frame optimization problems. The numerical experiments using challenging test examples of real-size steel frames confirm the efficiency of the MCC integrated metaheuristics versus their standard counterparts.Article Citation - WoS: 95Citation - Scopus: 118Adaptive Dimensional Search: a New Metaheuristic Algorithm for Discrete Truss Sizing Optimization(Pergamon-elsevier Science Ltd, 2015) Hasancebi, Oguzhan; Azad, Saeıd Kazemzadeh; Azad, Saeid Kazemzadeh; Azad, Saeıd Kazemzadeh; Department of Civil Engineering; Department of Civil EngineeringIn the present study a new metaheuristic algorithm called adaptive dimensional search (ADS) is proposed for discrete truss sizing optimization problems. The robustness of the ADS lies in the idea of updating search dimensionality ratio (SDR) parameter online during the search for a rapid and reliable convergence towards the optimum. In addition, several alternative stagnation-control strategies are integrated with the algorithm to escape from local optima, in which a limited uphill (non-improving) move is permitted when a stagnation state is detected in the course of optimization. Besides a remarkable computational efficiency, the ease of implementation and capability of locating promising solutions for challenging instances of practical design optimization are amongst the remarkable features of the proposed algorithm. The efficiency of the ADS is investigated and verified using two benchmark examples as well as three real-world problems of discrete sizing truss optimization. A comparison of the numerical results obtained using the ADS with those of other metaheuristic techniques indicates that the proposed algorithm is capable of locating improved solutions using much lesser computational effort. (C) 2015 Elsevier Ltd. All rights reserved.
