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Article Citation - WoS: 2Citation - Scopus: 2Structural Design Optimization of Multi-Layer Spherical Pressure Vessels: a Metaheuristic Approach(Springer, 2019) Akis, Tolga; Azad, Saeid KazemzadehThis study addresses the optimum design problem of multi-layer spherical pressure vessels based on von Mises yield criterion. In order to compute the structural responses under internal pressure, analytical solutions for one-, two-, and three-layer spherical pressure vessels are provided. A population-based metaheuristic algorithm is reformulated for optimum material selection as well as thickness optimization of multi-layer spherical pressure vessels. Furthermore, in order to enhance the computational efficiency of the optimization algorithm, upper bound strategy is also integrated with the algorithm for reducing the total number of structural response evaluations during the optimization iterations. The performance of the algorithm is investigated through weight and cost minimization of one-, two- and three-layer spherical pressure vessels and the results are presented in detail. The obtained numerical results, based on different internal pressures as well as vessel sizes, indicate the usefulness and efficiency of the employed methodology in optimum design of multi-layer spherical pressure vessels.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.
